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Journal articles on the topic 'Estimation du trafic'
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1
Beuthe, Michel, and Anne-Sophie De Saint Martin. "Les coûts et bénéfices du Canal du Centre." Recherches économiques de Louvain 56, no. 1 (1990): 79–112. http://dx.doi.org/10.1017/s0770451800003572.
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RésuméCet article présente une analyse coûts-bénéfices des travaux du canal du Centre. Les coûts des expropriations, de construction et d'opération du canal sont comparés aux économies de coûts de transport. Leur calcul est basé sur une estimation des trafics futurs du canal et sur une analyse détaillée des coûts d'opération des bateaux. Divers scénarios d'évolution des coûts et du trafic sont examinés. L'article conclut que ces investissements ne sont pas rentables. Il serait même plus économique de ne pas terminer les travaux et de fermer l'ancien canal existant.
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Cariñena Balaguer, Rafael, and Andrés Díaz Borrás. "La colonia genovesa en Valencia durante la guerra civil catalana: el secuestro de sus bienes en 1472." Anuario de Estudios Medievales 24, no. 1 (April 2, 2020): 131. http://dx.doi.org/10.3989/aem.1994.v24.968.
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Le commerce génois à Valence a été considéré historiquement comme une des activités les plus dynamiques de l'économie locale. Jusqu’à maintenant on estimait que le trafic ligure, qui avait établi des maisons commerciales dans la ville de Valence, de même que le trafic local étaient énormes. Cependant, on ne disposait pas d'un paramètre assez ajusté pour évaluer la véracité de cette affirmation. Grâce à la confiscation des biens génois, qui eut lieu en 1472, en raison des soupçons du roi Juan de Navarre concernant l'appui ligure aux rebelles catalans pendant la Guerre Civile, nous avons pu avanceroune estimation, partielle mais significative, de l'importance commerciale des maisons génoises établies à Valence au XVème siècle. Au contraire de l'opinion générale, le trafic génois ne devait pas être aussi puissant qu'il a été pretendu dans les études historiques citées, tant sur le plan des marchandises disponibles que sur celui du nombre de maisons commerciales établies à Valence.
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KOENIGUER, Elise, Jean-Marie Nicolas, Béatrice Pinel-Puyssegur, Jean-Michel Lagrange, and Fabrice Janez. "Visualisation des changements sur séries temporelles radar : méthode REACTIV évaluée à l'échelle mondiale sous Google Earth Engine." Revue Française de Photogrammétrie et de Télédétection, no. 217-218 (September 21, 2018): 99–108. http://dx.doi.org/10.52638/rfpt.2018.409.
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Cet article présente une méthode de visualisation d'une pile temporelle d'images SAR, appelée REACTIV, qui permet de faire ressortir en couleur les zones ayant subi des changements sur la période de temps observée. Cette méthode a été testée à grande échelle grâce à la plateforme Google Earth Engine. Elle est basée sur l'espace de visualisation HSV et n'exploite que des estimations dans le domaine temporel, sans aucune estimation spatiale. La saturation des couleurs est codée par le coefficient de variation temporel, dont plusieurs propriétés statistiques sont explicitées. Les limites de l'utilisation de la plateforme Google Earth Engine sont évaluées, et plusieurs cas d'applications sont proposés : agriculture, dynamique urbaine, trafic maritime.
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Jourquin, Bart. "Estimation de l'impact de l'internalisation des coûts externes du trafic de fret interurbain en Belgique." Reflets et perspectives de la vie économique XLIII, no. 4 (2004): 77. http://dx.doi.org/10.3917/rpve.434.0077.
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Chanut, S., and E. Chevallier. "Estimation des impacts atmosphériques des projets de gestion de trafic : de l’application des modèles théoriques sur des cas concrets*." Recherche Transports sécurité 2012, no. 01 (February 2013): 1–14. http://dx.doi.org/10.1007/s13547-011-0018-4.
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Ozguven, Eren Erman, and Kaan Ozbay. "Nonparametric Bayesian Estimation of Freeway Capacity Distribution from Censored Observations." Transportation Research Record: Journal of the Transportation Research Board 2061, no. 1 (January 2008): 20–29. http://dx.doi.org/10.3141/2061-03.
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Previous studies have been made of the usefulness and effectiveness of survival analysis in transportation and traffic engineering studies with incomplete data in which the Kaplan–Meier estimate is proposed for determining traffic capacity distribution. However, well-known estimators like Kaplan–Meier and Nelson–Aalen have several disadvantages that make it difficult to obtain the traffic capacity distribution. First, neither estimator is defined for all values of traffic flows possible. That is, the maximum flow followed by a breakdown defines the final point of the estimated distribution curve. Therefore, parametric fitting tools have to be applied to obtain the remaining portion of the curve. Moreover, the discontinuity and nonsmoothness of the Kaplan–Meier and Nelson–Aalen estimates make it difficult to ensure the robustness of the estimation. In this paper the Kaplan–Meier and Nelson–Aalen nonparametric estimators are used to obtain the traffic capacity function of four freeway sections. Then a Bayesian nonparametric estimator, which is shown to be a Bayesian extension of the Kaplan–Meier estimator, is introduced for estimating the capacity distribution. This estimator assumes a Dirichlet process prior for the survival function under the minimization of a squared-error loss function. The results indicate that the curves obtained by using the Bayesian estimation method are smoother than those obtained with the other estimator. This smoothness also ensures the continuity in the vicinity of censored observations. Furthermore, the Bayesian estimates can be obtained for any traffic flow value regardless of the availability of data only for certain ranges of observations (including censored data).
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Bresch, M., J. Shi, and R. Kokozinski. "Employing beam-forming for estimating the direction of arrival in a multi-path propagation environment." Advances in Radio Science 3 (May 12, 2005): 151–55. http://dx.doi.org/10.5194/ars-3-151-2005.
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Abstract. Due to recent researches on traffic accidents with vulnerable road users (VRUs), several measures revealed a great opportunity of reduction. However, all measures applied so far failed to reduce the number of traffic accidents if there is no line-of-sight. Therefore, a transponder signal is utilized to make the VRU visible. The motor vehicle carries a mobile receiver for VRU detection and location. The receiver employs digital beam-forming for estimating the direction of arrival (DOA) with an antenna array for RF ISM band. A sequence of DOA estimations is used for location and motion estimation purposes.
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Tao, Tao, Greg Lindsey, Raphael Stern, and Michael Levin. "The use of crowdsourced mobile data in estimating pedestrian and bicycle traffic: A systematic review." Journal of Transport and Land Use 17, no. 1 (February 1, 2024): 41–65. http://dx.doi.org/10.5198/jtlu.2024.2315.
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To address the need for better non-motorized traffic data, policymakers and researchers are collaborating to develop new approaches and methods for estimating pedestrian and bicyclist traffic volumes. Crowdsourced mobile data, which has higher spatial and temporal coverage and lower collection costs than data collected through traditional approaches, may help improve pedestrian and bicyclist traffic estimation despite their limitations or biases. This systemic literature review documents how researchers have used crowdsourced mobile data to estimate pedestrian and bicyclist traffic volumes. We find that one source of commercial fitness application data (i.e., Strava) has been used much more frequently than other crowdsourced mobile data, and that most studies have used crowdsourced mobile data to estimate bicyclist volumes. Comparatively few studies have estimated pedestrian volumes. The most common approach to the use of crowdsourced counts is as independent variables in direct demand models. Variables constructed from crowdsourced mobile data not only have significant correlations with observed counts in statistical models but also have larger relative importance than other factors in machine learning models. Studies also show that including crowdsourced mobile data can significantly improve estimation performance. Future research directions include application of crowdsourced mobile data in more pedestrian traffic estimations, comparison of the performance of different crowdsourced mobile data, incorporation of multiple data sources, and expansion of the methods using crowdsourced mobile data for non-motorized traffic estimation.
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Suyama, Emilio, Roberto C. Quinino, and Frederico R. B. Cruz. "Simple and Yet Efficient Estimators for Markovian Multiserver Queues." Mathematical Problems in Engineering 2018 (December 25, 2018): 1–7. http://dx.doi.org/10.1155/2018/3280846.
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Estimators for the parameters of the Markovian multiserver queues are presented, from samples that are the number of clients in the system at arbitrary points and their sojourn times. As estimation in queues is a recognizably difficult inferential problem, this study focuses on the estimators for the arrival rate, the service rate, and the ratio of these two rates, which is known as the traffic intensity. Simulations are performed to verify the quality of the estimations for sample sizes up to 400. This research also relates notable new insights, for example, that the maximum likelihood estimator for the traffic intensity is equivalent to its moment estimator. Some limitations of the results are presented along with a detailed numerical example and topics for future developments in this research area.
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Somplak, Radovan, Zlata Smidova, Veronika Smejkalova, and Vlastimir Nevrly. "Statistical Evaluation of Large-Scale Data Logistics System." MENDEL 24, no. 2 (December 21, 2018): 9–16. http://dx.doi.org/10.13164/mendel.2018.2.009.
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Data recording is struggling with the occurrence of errors, which worsen the accuracy of follow-up calculations. Achievement of satisfactory results requires the data processing to eliminate the influence of errors. This paper applies a data reconciliation technique for mining of data from ecording movement vehicles. The database collects information about the start and end point of the route (GPS coordinates) and total duration.The presented methodology smooths available data and allows to obtain an estimation of transportation time through individual parts of the entire recorded route. This process allows obtaining valuable information which can be used for further transportation planning. First, the proposed mathematical model is tested on simplifled example. The real data application requires necessary preprocessing within which anticipated routes are designed. Thus, the database is supplemented with information on the probable speed of the vehicle. The mathematical model is based on weighted least squares data reconciliation which is organized iteratively. Due to the time-consuming calculation, the linearised model is computed to initialize the values for a complex model. The attention is also paid to the weight setting. The weighing system is designed to reflect the quality of specific data and the dependence on the frequency of trafic. In this respect, the model is not strict, which leaves the possibility to adapt to the current data. The case study focuses on the GPS data of shipping vehicles in the particular city in the Czech Republic with several types of roads.
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Poryazov, Stoyan, Velin Andonov, Emiliya Saranova, and Krassimir Atanassov. "Two Approaches to the Traffic Quality Intuitionistic Fuzzy Estimation of Service Compositions." Mathematics 10, no. 23 (November 24, 2022): 4439. http://dx.doi.org/10.3390/math10234439.
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Recently, intuitionistic fuzzy pairs have been used as uncertainty estimations of the request services in service systems. In the present paper, three intuitionistic fuzzy characterizations of virtual service devices are specified: intuitionistic fuzzy traffic estimation, intuitionistic fuzzy flow estimation and intuitionistic fuzzy estimation about probability. Discussed herein are two approaches to the intuitionistic fuzzy estimation of the uncertainty of compositions of services. One of the approaches is based on the definitions of the intuitionistic fuzzy pairs for one service device. The other approach is based on the aggregation operators over intuitionistic fuzzy pairs. A total of six intuitionistic fuzzy estimations of the uncertainty of comprise service device are proposed. The proposed uncertainty estimations allow for the definition of new Quality of Service (QoS) indicators and can be used to determine the quality of service compositions across a wide range of service systems.
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El Mekkaoui, Sara, Loubna Benabbou, Stéphane Caron, and Abdelaziz Berrado. "Deep Learning-Based Ship Speed Prediction for Intelligent Maritime Traffic Management." Journal of Marine Science and Engineering 11, no. 1 (January 12, 2023): 191. http://dx.doi.org/10.3390/jmse11010191.
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Improving maritime operations planning and scheduling can play an important role in enhancing the sector’s performance and competitiveness. In this context, accurate ship speed estimation is crucial to ensure efficient maritime traffic management. This study addresses the problem of ship speed prediction from a Maritime Vessel Services perspective in an area of the Saint Lawrence Seaway. The challenge is to build a real-time predictive model that accommodates different routes and vessel types. This study proposes a data-driven solution based on deep learning sequence methods and historical ship trip data to predict ship speeds at different steps of a voyage. It compares three different sequence models and shows that they outperform the baseline ship speed rates used by the VTS. The findings suggest that deep learning models combined with maritime data can leverage the challenge of estimating ship speed. The proposed solution could provide accurate and real-time estimations of ship speed to improve shipping operational efficiency, navigation safety and security, and ship emissions estimation and monitoring.
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Wang, Jiawen, Yinsong Wang, Meiping Yun, and Xiaoguang Yang. "Development of Urban Road Network Traffic State Dynamic Estimation Method." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/714149.
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Traffic state estimation is a key problem with considerable implications in modern traffic management. A simple, general, and complete approach to the design of urban network traffic state and phase estimator has been developed in this paper. A uniform traffic state dynamic estimation method structure is designed which consists of three steps. (1) Floating-car data and radio frequency identification data preprocessing method is proposed to remove the abnormal data and finish the map matching process. (2) Section speed estimation method is proposed based on the degree of confidence. (3) Traffic phase identification method is proposed based on the estimated section speed. A number of simulation and field investigations have been conducted to test the estimator performance. The investigation results indicate that the proposed approach is of high accuracy and smoothness on the section speed estimation and effectively eliminates the influence of abnormal data fluctuations and insufficient data. And the traffic phase identification method can effectively filter out the abnormal distortion of estimated section speed around the threshold value and modify the phase step of traffic status caused by abnormal data.
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Kirova, Veronika, Eduard Siemens, Dmitry Kachan, Oksana Vasylenko, and Kirill Karpov. "Optimization of Probe Train Size for Available Bandwidth Estimation in High-speed Networks." MATEC Web of Conferences 208 (2018): 02001. http://dx.doi.org/10.1051/matecconf/201820802001.
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Available bandwidth parameter is a crucial characteristic in terms of networking and data transmission. The beforehand knowledge of its value and use of this parameter in various traffic engineering algorithms and QoS calculations is a key for high-efficient multigigabit data transport in nowadays networks. The challenge in available bandwidth estimations is not only in its accuracy and processing speed but also in the reduction of the amount of probe traffic injected into the network by keeping an adequate level of estimation accuracy. In this paper we extend existing active probing measurement algorithms for end-to-end available bandwidth estimation along with methods to reduce estimation times and amount of injected traffic while keeping measurement accuracy constant and even reducing the uncertainty of estimations. The main goal of this research was to detect a sufficient ratio of MTU, packet train size with the link capacity and available bandwidth (AvB) in up to 10 Gbps networks. In order to explore measurement accuracy under different conditions, a new tool for the AvB estimation named Kite2 has been developed and is presented in the paper. Comparative performance of AvB estimations using Kite2, Kite and Yaz is presented. Finally we calculate with statistical means dependency between the estimation error probability, measurement probing overhead and the measurement time.
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Xu, Jiajie, Saijun Xu, Rui Zhou, Chengfei Liu, An Liu, and Lei Zhao. "TAML: A Traffic-aware Multi-task Learning Model for Estimating Travel Time." ACM Transactions on Intelligent Systems and Technology 12, no. 6 (December 31, 2021): 1–14. http://dx.doi.org/10.1145/3466686.
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Travel time estimation has been recognized as an important research topic that can find broad applications. Existing approaches aim to explore mobility patterns via trajectory embedding for travel time estimation. Though state-of-the-art methods utilize estimated traffic condition (by explicit features such as average traffic speed) for auxiliary supervision of travel time estimation, they fail to model their mutual influence and result in inaccuracy accordingly. To this end, in this article, we propose an improved traffic-aware model, called TAML, which adopts a multi-task learning network to integrate a travel time estimator and a traffic estimator in a shared space and improves the accuracy of estimation by enhanced representation of traffic condition, such that more meaningful implicit features are fully captured. In TAML, multi-task learning is further applied for travel time estimation in multi-granularities (including road segment, sub-path, and entire path). The multiple loss functions are combined by considering the homoscedastic uncertainty of each task. Extensive experiments on two real trajectory datasets demonstrate the effectiveness of our proposed methods.
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R, Vinothkanna. "A Survey on Novel Estimation Approach of Motion Controllers for Self-Driving Cars." December 2020 2, no. 4 (January 13, 2021): 211–19. http://dx.doi.org/10.36548/jei.2020.4.003.
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The motion planning framework is one of the challenging tasks in autonomous driving cars. During motion planning, predicting of trajectory is computed by Gaussian propagation. Recently, the localization uncertainty control will be estimating by Gaussian framework. This estimation suffers from real time constraint distribution for (Global Positioning System) GPS error. In this research article compared novel motion planning methods and concluding the suitable estimating algorithm depends on the two different real time traffic conditions. One is the realistic unusual traffic and complex target is another one. The real time platform is used to measure the several estimation methods for motion planning. Our research article is that comparing novel estimation methods in two different real time environments and an identifying better estimation method for that. Our suggesting idea is that the autonomous vehicle uncertainty control is estimating by modified version of action based coarse trajectory planning. Our suggesting framework permits the planner to avoid complex and unusual traffic (uncertainty condition) efficiently. Our proposed case studies offer to choose effectiveness framework for complex mode of surrounding environment.
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Kaklauskas, Liudvikas, and Leonidas Sakalauskas. "Tinklo apkrovos savastingumo tyrimas realiu laiku." Informacijos mokslai 53 (January 1, 2010): 100–105. http://dx.doi.org/10.15388/im.2010.0.3181.
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Straipsnyje analizuojami indikatoriai, taikomi tinklo apkrovos savastingumui tirti: Hursto indeksas, stabilumo indeksas, IR (Increment Ratio) statistika. Kompiuteriniu modeliavimu ištirtas šių indikatorių tinkamumas tinklo apkrovos savastingumui vertinti realiu laiku. Sukurta programinių modulių biblioteka SSE (Self-Similarity Estimator), skirta fiksuoti ir agreguoti tinklo duomenų paketus, vertinanti tinklo apkrovos srautų savastingumą realiu laiku. Naudojant SSE programinių modulių biblioteką, suformuotų laiko eilučių Hursto indeksas ir IR statistika apskaičiuotos naudojant analitines formules, o stabilumo indeksas – robastiniu empirinių kvantilių regresijos metodu. Modulių bibliotekos SSE analizės efektyvumas ištirtas kompiuterinio modeliavimo būdu apskaičiuojant savastingumo indikatorius stabiliųjų procesų realizacijoms.Pagrindiniai žodžiai: savastingumas (self-similarity), Hursto indeksas, stabilumo indeksas, IR statistika.The Real-time Mode Research of Network Traffic FractalityLiudvikas Kaklauskas, Leonidas Sakalauskas Summaryhe article analyses the indicators implemented for investigating the network self-similarity: the Hurst index, stability index, IR (Increment Ratio) statistics. The suitability of these indicators for the on-line estimation of network traffic self-similarity was investigated by applying computer-based modelling. The software SSE (Self-Similarity Estimator) module library was developed; it was designed for the recording and aggregation of network traffic packages as well as for the on-line estimation of network traffic self-similarity. By using the SSE software module library, the Hurst index and the IR statistics of time series were estimated by applying analytic formulas, and the index of stability was estimated applying the robust method of regression of empirical quantiles. The efficiency of the analysis of the SSE module library was investigated by estimating the self-similarity indicators for realisation of the stabile processes while applying the method of computer-based modelling.
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Sankaranarayanan, Manipriya, Mala C., and Samson Mathew. "Road Traffic Congestion (TraCo) Estimation Using Multi-Layer Continuous Virtual Loop (MCVL)." International Journal of Intelligent Information Technologies 17, no. 2 (April 2021): 46–71. http://dx.doi.org/10.4018/ijiit.2021040103.
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Any road traffic management application of intelligent transportation systems (ITS) requires traffic characteristics data such as vehicle density, speed, etc. This paper proposes a robust and novel vehicle detection framework known as multi-layer continuous virtual loop (MCVL) that uses computer vision techniques on road traffic video to estimate traffic characteristics. Estimations of traffic data such as speed, area occupancy and an exclusive spatial feature named as corner detail value (CDV) acquired using MCVL are proposed. Further, the estimation of traffic congestion (TraCo) level using these parameters is also presented. The performances of the entire framework and TraCo estimation are evaluated using several benchmark traffic video datasets and the results are presented. The results show that the improved accuracy in vehicle detection process using MCVL subsequently improves the precision of TraCo estimation. This also means that the proposed framework is well suited to applications that need traffic characteristics to update their traffic information system in real time.
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Reiman, Martin I. "Asymptotic Properties of a Recursive Procedure for Simultaneous Estimation." Probability in the Engineering and Informational Sciences 4, no. 4 (October 1990): 461–75. http://dx.doi.org/10.1017/s0269964800001765.
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In this paper we consider a problem that arises in estimating the heavy traffic limit of a sojourn time distribution in a queueing network during the course of a medium traffic simulation. We need to estimate α = E[f(γ, M)], where γ is an unknown constant and M a random variable. More specifically, we are given an iid sequence of random vectors {(Xi, Mi), 1 ≤ i ≤ n}, with γ = E[Xi] and Mi having the same distribution as M.For known γ, we have a standard estimation problem, which we describe here. The standard estimate is unbiased and asymptotically (as n → 8 ) consistent. There is also a central limit theorem for this estimator. For unknown γ, we provide two estimation procedures, one that requires two passes through the data (as well as storage of {Mi, 1 ≤ i ≤ n}), and another one, which is recursive, requiring only one pass through and bounded storage. The estimators obtained from these two procedures are shown to be strongly consistent, and central limit theorems are also proven for them.
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Grumert, Ellen F., and Andreas Tapani. "Traffic State Estimation Using Connected Vehicles and Stationary Detectors." Journal of Advanced Transportation 2018 (2018): 1–14. http://dx.doi.org/10.1155/2018/4106086.
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Real-time traffic state estimation is of importance for efficient traffic management. This is especially the case for traffic management systems that require fast detection of changes in the traffic conditions in order to apply an effective control measure. In this paper, we propose a method for estimating the traffic state and speed and density, by using connected vehicles combined with stationary detectors. The aim is to allow fast and accurate estimation of changes in the traffic conditions. The proposed method does only require information about the speed and the position of connected vehicles and can make use of sparsely located stationary detectors to limit the dependence on the infrastructure equipment. An evaluation of the proposed method is carried out by microscopic traffic simulation. The traffic state estimated using the proposed method is compared to the true simulated traffic state. Further, the density estimates are compared to density estimates from one detector-based method, one combined method, and one connected-vehicle-based method. The results of the study show that the proposed method is a promising alternative for estimating the traffic state in traffic management applications.
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Kumapley, Robert K., and Jon D. Fricker. "Review of Methods for Estimating Vehicle Miles Traveled." Transportation Research Record: Journal of the Transportation Research Board 1551, no. 1 (January 1996): 59–66. http://dx.doi.org/10.1177/0361198196155100108.
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Estimates of vehicle miles traveled (VMT) are used extensively in transportation planning for allocating resources, estimating vehicle emissions, computing energy consumption, and assessing traffic impact. The estimates used in these applications usually come from different sources. For an objective comparison of VMT estimates from different methods, the principles and assumptions supporting the methods and the potential sources of error associated with the methods must be clearly understood. Methods of estimating VMT, including those used by the Indiana Department of Transportation (INDOT), are reviewed. Also presented is a comparison of statewide VMT estimates in Indiana from INDOT's traffic count–based method and a non–traffic-data cross-classification VMT estimation model developed for INDOT. The cross-classification model is an independent source of statewide VMT estimates in Indiana to supplement INDOT's traffic count–based estimates. The results of the comparison indicate that INDOT's traffic count–based estimates can be 10 to 20 percent higher than the estimates from the cross-classification VMT estimation model.
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Tian, Cheng, Bo Leng, Xinchen Hou, Lu Xiong, and Chao Huang. "Multi-Sensor Fusion Based Estimation of Tire-Road Peak Adhesion Coefficient Considering Model Uncertainty." Remote Sensing 14, no. 21 (November 5, 2022): 5583. http://dx.doi.org/10.3390/rs14215583.
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The tire-road peak adhesion coefficient (TRPAC), which cannot be directly measured by on-board sensors, is essential to road traffic safety. Reliable TRPAC estimation can not only serve the vehicle active safety system, but also benefit the safety of other traffic participants. In this paper, a TRPAC fusion estimation method considering model uncertainty is proposed. Based on virtual sensing theory, an image-based fusion estimator considering the uncertainty of the deep-learning model and the kinematic model is designed to realize the accurate classification of the road surface condition on which the vehicle will travel in the future. Then, a dynamics-image-based fusion estimator considering the uncertainty of visual information is proposed based on gain scheduling theory. The results of simulation and real vehicle experiments show that the proposed fusion estimation method can make full use of multisource sensor information, and has significant advantages in estimation accuracy, convergence speed and estimation robustness compared with other single-source-based estimators.
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Yi, Ting, and Billy M. Williams. "Dynamic Traffic Flow Model for Travel Time Estimation." Transportation Research Record: Journal of the Transportation Research Board 2526, no. 1 (January 2015): 70–78. http://dx.doi.org/10.3141/2526-08.
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Travel time, as a fundamental measurement for intelligent transportation systems, is becoming increasingly important. Because of the wide deployment of fixed-point detectors on freeways, if travel time can be accurately estimated from point detector data, the indirect estimation method is cost-effective and widely applicable. This paper presents a modified dynamic traffic flow model for accurately estimating the travel time of freeway links under transition and congestion conditions with fixed-point detector data. The modified estimation model is based on a thorough analysis of the dynamic traffic flow model. The applications and the limitations of the model are analyzed for theory, equation derivation, and modifications. Through a simulation study and real traffic data, the (modified) dynamic models are compared according to performance measurements. A comparison of the estimated results and measurement errors shows the accuracy of the modified dynamic model for estimating the travel times of freeway links under transition and congestion traffic conditions.
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Kim, Hoe Kyoung, Younshik Chung, and Minjeong Kim. "Effect of Enhanced ADAS Camera Capability on Traffic State Estimation." Sensors 21, no. 6 (March 12, 2021): 1996. http://dx.doi.org/10.3390/s21061996.
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Traffic flow data, such as flow, density and speed, are crucial for transportation planning and traffic system operation. Recently, a novel traffic state estimating method was proposed using the distance to a leading vehicle measured by an advanced driver assistance system (ADAS) camera. This study examined the effect of an ADAS camera with enhanced capabilities on traffic state estimation using image-based vehicle identification technology. Considering the realistic distance error of the ADAS camera from the field experiment, a microscopic simulation model, VISSIM, was employed with multiple underlying parameters such as the number of lanes, traffic demand, the penetration rate of ADAS vehicles and the spatiotemporal range of the estimation area. Although the enhanced functions of the ADAS camera did not affect the accuracy of the traffic state estimates significantly, the ADAS camera can be used for traffic state estimation. Furthermore, the vehicle identification distance of the ADAS camera and traffic conditions with more lanes did not always ensure better accuracy of the estimates. Instead, it is recommended that transportation planners and traffic engineering practitioners carefully select the relevant parameters and their range to ensure a certain level of accuracy for traffic state estimates that suit their purposes.
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С. И., Носков,, and Базилевский, М. П. "Multiple Lv-estimation of Linear Regression Models." Успехи кибернетики / Russian Journal of Cybernetics, no. 4(12) (December 28, 2022): 32–40. http://dx.doi.org/10.51790/2712-9942-2022-3-4-04.
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для оценки моделей множественной линейной регрессии существует много различных математических методов: наименьших квадратов, модулей, антиробастного оценивания, Lv-оценивания, множественного оценивания. Целью данной работы является обобщение указанных методов оценивания единой функцией потерь. Сначала была сформулирована задача оценивания, в которой в качестве критериев минимизации выступают критерии для антиробастного и Lv-оценивания. Недостатком сформулированной задачи является то, что для ее численного решения затруднительно определять начальные значения параметров, поскольку переменные могут иметь разные масштабы. Кроме того, функция потерь для этой задачи является неоднородной, что также затрудняет процесс оценивания. Для решения этих проблем введен новый критерий, равный критерию антиробастного оценивания, возведенному в степень v. С помощью него и функции потерь для Lv-оценивания сформулирована задача множественного Lv-оценивания. Функционал этой задачи является однородным, поэтому для проведения множественного Lv-оценивания целесообразно нормировать исходные переменные и переходить к оценкам стандартизованной линейной регрессии. Предложен алгоритм, по которому рекомендуется проводить множественное Lv-оценивание. В результате проведения множественного Lv-оценивания формируется множество, содержащее оценки линейной регрессии, полученные как известными методами, так и новыми. Правильный выбор наилучших из полученного множества оценок пока остается открытой научной задачей. С помощью предложенного множественного Lv-оценивания успешно решена задача моделирования железнодорожных пассажирских перевозок Иркутской области. there are many methods for estimating multiple linear regression models: ordinary least squares, least absolute deviations, anti-robust estimation, Lv-estimation, and multiple estimations. The purpose of this work is to generalize these methods by a loss function. First, an estimation problem was formulated where the minimization criteria are the anti-robust and Lv-estimations. The disadvantage of this problem statement is that it is difficult to determine the initial values of the parameters for a numerical solution, since the variables may have different scales. Besides, the loss function is non-uniform, which also complicates the estimation. To solve these problems, we introduced a new criterion, equal to the anti-robust estimation criterion raised to the power v. We stated the problem of multiple Lv-estimation using the new criterion and the loss function. The functional of this problem is homogeneous, therefore, for multiple Lv-estimations, it is advisable to normalize the initial variables and then apply the standardized linear regression estimates. We also developed an algorithm for multiple Lv-estimations. A result of such estimations is a set containing linear regression estimates obtained both by the existing and new methods. The optimal choice of the best estimates from the set of estimates remains an open problem. We successfully simulated the passenger railway traffic in the Irkutsk region with the proposed multiple Lv-estimations.
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Park, Dongjoo, Soyoung You, Jeonghyun Rho, Hanseon Cho, and Kangdae Lee. "Investigating optimal aggregation interval sizes of loop detector data for freeway travel-time estimation and prediction." Canadian Journal of Civil Engineering 36, no. 4 (April 2009): 580–91. http://dx.doi.org/10.1139/l08-129.
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With recent increases in the deployment of intelligent transportation system (ITS) technologies, traffic management centers have the ability to obtain and archive large amounts of data regarding the traffic system. These data can then be employed in estimations of current conditions and the prediction of future conditions on the roadway network. In this paper, we propose a general solution methodology for the identification of the optimal aggregation interval sizes of loop detector data for four scenarios (i) link travel-time estimation, (ii) corridor / route travel-time estimation, (iii) link travel-time forecasting, and (iv) corridor / route travel-time forecasting. This study applied cross validated mean square error (CVMSE) model for the link and route travel-time estimations, and a forecasting mean square error (FMSE) model for the link and corridor / route travel-time forecasting. These models were applied to loop detector data obtained from the Kyeongbu expressway in Korea. It was found that the optimal aggregation sizes for the travel-time estimation and forecasting were 3 to 5 min and 10 to 20 min, respectively.
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Yu, Minghe, Jianhua Feng, Tianmiao Zhang, and Tiancheng Zhang. "A New Approach to Regional Traffic Estimation for Intelligent Transport Systems." Journal of Advanced Transportation 2022 (June 28, 2022): 1–9. http://dx.doi.org/10.1155/2022/8588911.
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With the great development of urban transportation systems, immediate urban traffic information has become an essential resource for the public. Traffic estimation is to predict current or future traffic situation (traffic speed and/or volume) in a road or a region of a city, and can benefit our daily life from many aspects, such as routing planning and traffic management. Existing works focus on estimating future traffic for individual road segments from a perspective of fine-grained level. This paper presents a new approach to estimating future traffic from a perspective of coarse-grained level, by which we estimate the traffic situation of a region, instead of an individual road segment. We propose a new concept about regional traffic named Ω-region, which aims to reflect the traffic situation of a region precisely. Two challenges in the regional traffic estimation problem are how to partition the road network into reasonable regions and how to estimate the regional traffic effectively. To address these challenges, first we define reasonable regions Ω-regions with traffic situations so that the all the road segment in the region has similar traffic. Then, we propose a three-phase partition method to divide the road network into Ω-regions based on historical trajectory data. Thirdly, we propose an effective linear-based model to estimate regional traffic. Experimental results on real-world dataset show that our proposed method achieves high performance.
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Qin, Hongshuai, and Huibin Qin. "Image-Based Dedicated Methods of Night Traffic Visibility Estimation." Applied Sciences 10, no. 2 (January 7, 2020): 440. http://dx.doi.org/10.3390/app10020440.
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Traffic visibility is an essential reference for safe driving. Nighttime conditions add to the difficulty of estimating traffic visibility. To estimate the visibility in nighttime traffic images, we propose a Traffic Sensibility Visibility Estimation (TSVE) algorithm that combines laser transmission and image processing and needs no reference to the corresponding fog-free images and camera calibration. The information required is first obtained via the roadside equipment which collects environmental data and captures road images and then analyzed locally or remotely. The proposed analysis includes calculating the current atmospheric transmissivity with the laser atmospheric transmission theory and acquiring image features by using the cameras and the adjustable brightness target. Image analysis is performed using two image processing algorithms, namely, dark channel prior (DCP) and image brightness contrast. Finally, to improve the accuracy of visibility estimation, multiple nonlinear regression (MNLR) is performed on the various visibility indicators obtained by the two methods. Extensive on-site measurements analysis confirms the advantages of TSVE. Compared with other visibility estimation methods, such as the laser atmospheric transmission theory and image analysis method, TSVE significantly decreases the estimation errors.
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Davis, Gary A. "Estimating Traffic Accident Rates While Accounting for Traffic-Volume Estimation Error: A Gibbs Sampling Approach." Transportation Research Record: Journal of the Transportation Research Board 1717, no. 1 (January 2000): 94–101. http://dx.doi.org/10.3141/1717-12.
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Traffic-accident rates that are estimated for individual roadway sites are often used to identify potentially hazardous locations. Occasionally they are used to test whether an accident countermeasure is associated with a statistically significant change in accident rate. In assessing the uncertainty attached to estimated accident rates, it is often implicitly assumed that the total traffic at a site is known with certainty, when in actuality the total traffic almost always must be estimated from a short sample of traffic counts. This introduces estimation error, which, if ignored, can lead one to overstate the accuracy of an accident-rate estimate. An explanation is provided about how Bayes estimates of accident rates, which explicitly account for total traffic estimation error, can be computed readily using a (relatively) new estimation method called “Gibbs sampling.” A model of how traffic-count samples are related to total traffic is incorporated from earlier work done by the author and his students. In tests conducted using accident counts and traffic data from 17 automatic traffic-recorder sites in Minnesota, it was found that, when using a 2-day traffic-count sample, the traditional method for estimating accidents rates understated the likely error of these estimates by 12 to 40 percent, depending on the site.
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Wessels, Marlene, Heiko Hecht, Thirsa Huisman, and Daniel Oberfeld. "Trial-by-trial feedback fails to improve the consideration of acceleration in visual time-to-collision estimation." PLOS ONE 18, no. 8 (August 2, 2023): e0288206. http://dx.doi.org/10.1371/journal.pone.0288206.
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When judging the time-to-collision (TTC) of visually presented accelerating vehicles, untrained observers do not adequately account for acceleration (second-order information). Instead, their estimations only rely on vehicle distance and velocity (first-order information). As a result, they systemically overestimate the TTC for accelerating objects, which represents a potential risk for pedestrians in traffic situations because it might trigger unsafe road-crossing behavior. Can training help reduce these estimation errors? In this study, we tested whether training with trial-by-trial feedback about the signed deviation of the estimated from the actual TTC can improve TTC estimation accuracy for accelerating vehicles. Using a prediction-motion paradigm, we measured the estimated TTCs of twenty participants for constant-velocity and accelerated vehicle approaches, from a pedestrian’s perspective in a VR traffic simulation. The experiment included three blocks, of which only the second block provided trial-by-trial feedback about the TTC estimation accuracy. Participants adjusted their estimations during and after the feedback, but they failed to differentiate between accelerated and constant-velocity approaches. Thus, the feedback did not help them account for acceleration. The results suggest that a safety training program based on trial-by-trial feedback is not a promising countermeasure against pedestrians’ erroneous TTC estimation for accelerating objects.
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Arriffin, Maizatul Najihah, Salama A. Mostafa, Umar Farooq Khattak, Mustafa Musa Jaber, Zirawani Baharum, Defni, and Taufik Gusman. "Vehicles Speed Estimation Model from Video Streams for Automatic Traffic Flow Analysis Systems." JOIV : International Journal on Informatics Visualization 7, no. 2 (May 11, 2023): 295. http://dx.doi.org/10.30630/joiv.7.2.1820.
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Image and video processing have been widely used to provide traffic parameters, which will be used to improve certain areas of traffic operations. This research aims to develop a model for estimating vehicle speed from video streams to support traffic flow analysis (TFA) systems. Subsequently, this paper proposes a vehicle speed estimation model with three main stages of achieving speed estimation: (1) pre-processing, (2) segmentation, and (3) speed detection. The model uses a bilateral filter in the pre-processing strategy to provide free-shadow image quality and sharpen the image. Gaussian filter and active contour are used to detect and track objects of interest in the image. The Pinhole model is used to assess the real distance of the item within the image sequence for speed estimation. Kalman filter and optical flow are used to flatten vehicle speed and acceleration uncertainties. This model is evaluated with a dataset that consists of video recordings of moving vehicles at traffic light junctions on the urban roadway. The average percentage for speed estimation error is 20.86%. The average percentage for accuracy obtained is 79.14%, and the overall average precision of 0.08.
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Chandu, R. Sai, G. Venkata Krishna, T. Karthik, Sk Firowz, Mr Jilani Basha, and Dr K. G. S. Venkatesan. "Vehicle Speed Estimation and Traffic Tracking System Using Machine Learning." International Journal of Innovative Research in Engineering and Management 9, no. 2 (April 29, 2022): 661–66. http://dx.doi.org/10.55524/ijirem.2022.9.2.106.
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In this work, we deploy a real-time method for classifying vehicles and estimating their speeds using footage captured by traffic cameras along motorways. Basic techniques in traffic analysis include the forecasting of traffic flows, the discovery of anomalies, the re-identification of vehicles, and the tracking of moving vehicles. One of the most actively studied areas of these applications is traffic flow prediction, often known as vehicle speed estimation. In this work, we estimate vehicle speeds within classes using feature tracking and neighbor discovery techniques.
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Mądziel, Maksymilian. "Vehicle Emission Models and Traffic Simulators: A Review." Energies 16, no. 9 (May 7, 2023): 3941. http://dx.doi.org/10.3390/en16093941.
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Accurate estimations and assessments of vehicle emissions can support decision-making processes. Current emission estimation tools involve several calculation methods that provide estimates of the exhaust components that result from driving on urban arterial roads. This is an important consideration, as the emissions generated have a direct impact on the health of pedestrians near the roads. In recent years, there has been an increase in the use of emission models, especially in combination with traffic simulator models. This is because it is very difficult to obtain an actual measurement of road emissions for all vehicles travelling along the analysed road section. This paper concerns a review of selected traffic simulations and the estimation of exhaust gas components models. The models presented have been aggregated into a group with respect to their scale of accuracy as micro, meso, and macro. This paper also presents an overview of selected works that combine both traffic and emission models. The presented literature review also emphasises the proper calibration process of simulation models as the most important factor in obtaining accurate estimates. This work also contains information and recommendations on modelling that may be helpful in selecting appropriate emission estimation tools to support decision-making processes for, e.g., road managers.
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Körber, Moritz, Jonas Radlmayr, and Klaus Bengler. "Bayesian Highest Density Intervals of Take-Over Times for Highly Automated Driving in Different Traffic Densities." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 60, no. 1 (September 2016): 2009–13. http://dx.doi.org/10.1177/1541931213601457.
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Current human factors research on automated driving aims to ensure its safe introduction into road traffic. Although informative results are crucial for this purpose, most studies rely on point estimates and dichotomous reject-nonreject decisions that have been declared obsolete by more recent statistical approaches like new statistics (Cumming, 2014) or Bayesian parameter estimation (Kruschke, 2015). In this work, we show the objective advantages of Bayesian parameter estimation and demonstrate its abundance of information on parameters. In Study 1, we estimate take-over times with a relatively uninformed prior distribution. In Study 2, the resulting posterior distributions of Study 1 were then used as informed prior distributions for interval estimations of mean, standard deviation and distribution shape of take-over time in different traffic densities. We obtained 95 % credible interval widths between 490 ms and 600 ms for mean take-over times, depending on the condition. These intervals include the 95 % most probable values of the mean take-over time and represent a quantification of uncertainty in the estimation. Given the data and the experimental conditions, a take-over requires most likely 2.51 seconds [2.27, 2.76] when there is no traffic, 3.40 seconds [3.11, 3.71] in medium traffic and 3.50 seconds [3.21, 3.78] in high traffic. Bayesian model comparison with Bayes Factor is discussed as an alternative approach in conclusion.
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Andonov, Velin, Stoyan Poryazov, and Emiliya Saranova. "QoS characterization of some service compositions based on intuitionistic fuzzy pairs." Notes on Intuitionistic Fuzzy Sets 30, no. 2 (July 1, 2024): 190–202. http://dx.doi.org/10.7546/nifs.2024.30.2.190-202.
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In recent years, a new approach to the estimation of uncertainty in service systems has been developed. It is based on a causal characterization of the traffic in virtual service devices and makes use of the notion of an intuitionistic fuzzy pair. In a series of papers, this approach has been used to obtain quality of service estimations of various compositions of services. In the present paper, an overview of the main results related to the estimation of uncertainty in service compositions is presented.
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BARCHAŃSKI, Adrian, and Renata ŻOCHOWSKA. "PRACTICAL ASPECTS OF MEASURING CRITICAL GAPS AND FOLLOW-UP TIMES AT MEDIAN UNCONTROLLED T-INTERSECTIONS." Scientific Journal of Silesian University of Technology. Series Transport 113 (December 1, 2021): 17–28. http://dx.doi.org/10.20858/sjsutst.2021.113.2.
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Estimation of critical gaps and follow-up times between vehicles at uncontrolled intersections is an essential step in estimating the capacity of these objects and assessment of traffic conditions. Therefore, measurements of these parameters should be properly prepared and implemented. This paper presents issues related to the performance of field tests at median uncontrolled T-intersection. Measurements included both critical gaps and follow-up times. Based on the collected material, the authors identified problems occurring during traffic observation. Analyzed intersections were located both within and outside built-up areas. Furthermore, this article discusses the influence of selected factors on the accuracy of estimating the critical gaps and follow-up times and formulates the principles of conducting traffic measurements at selected types of intersections.
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Hummody, Mohammad Ahmad. "Estimation of Missing Left Turning Movement For Intersections Traffic Volume Count." Tikrit Journal of Engineering Sciences 13, no. 2 (June 30, 2006): 39–66. http://dx.doi.org/10.25130/tjes.13.2.03.
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Intersection traffic volume count must be in of the accurate data, because it is a crucial in the calibration and validation of traffic demand models. It is process in a continuous manner done by the analyst, planner or designer. Many procedures were recently produced to estimate the intersection turning movement matrix. Sometimes, these procedures of traffic volume count may have unusual, unavailable or missing values especially for the left turn movement, which is more effective in capacity analysis. Typical four mathematical and statistical methods of estimating the missing left turn movement volume were developed for about twenty signalized intersections. The most significant one is the typical curve estimation method. It is a power curve and in a simple formula compared with several other imputation techniques. This method can be superior to substitute the other methods in estimating the intersection traffic volume matrices.
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M. Ahmed, Rania, Zainab A. Alkaissi, and Ruba Y. Hussain. "TRAVEL TIME ANALYSIS OF SELECTED URBAN STREETS IN BAGHDAD CITY." Journal of Engineering and Sustainable Development 25, Special (September 20, 2021): 3–157. http://dx.doi.org/10.31272/jeasd.conf.2.3.15.
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Estimating travel time and measuring speed are critical for increasing the efficiency and safety of traffic road networks. This study presents an investigation of arterial travel time estimation for vital routes in Baghdad city. These estimations including speeds, stops, and delays were computed via GPS device and compared to those currently used to quantify congestion and travel time reliability. The study involved a 45-day survey of private vehicles in Baghdad utilizing a Global Positioning System (GPS) probe to collect data on traffic performance metrics for analysis in a GIS context. It was found that the proposed travel time performance measures show definite differences in estimates of peak-hour travel time as compared with weekend travel time. Route (1) from Bayaa intersection - Bab Al-Mutham intersection (through highway) produced a travel time of 165 minutes and 136 minutes for Bayaa intersection - Bab Al-Mutham intersection (through downtown). The travel speed of routes 1 and 2 are observed near 25 kmph which is below the local speed limit of 70 kmph. The maximum travel time of routes 1 and 2 are 71 minutes and 37 minutes, respectively. While delay time was observed 45 and 20 minutes due to traffic congestion on route 1 and 2, respectively. The majority of vehicles are capable of traveling at normal speeds, with relatively few exceeding them.
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Mustafa, Riad, and Ming Zhong. "Challenges and Opportunities in Applying High-Fidelity Travel Demand Model for Improved Network-Wide Traffic Estimation: A Review and Discussion." Open Transportation Journal 8, no. 1 (February 21, 2014): 1–18. http://dx.doi.org/10.2174/1874447801408010001.
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Abstract: Estimating traffic volume at a link level is important to transportation planners, traffic engineers, and policy makers. More specifically, this vital parameter has been used in transportation planning, traffic operations, highway geometric design, pavement design, and resource allocation. However, traditional factor approach, regression-‐based models, and artificial neural network models failed to present network-‐wide traffic volume estimates because they rely on traffic counts for model development, and they all have inherent weaknesses. A review to previous research work and the state-‐of-‐practice clearly indicates that the Traditional Four-step Travel Demand Model (TFTDM) was generally based on large traffic analysis zones (TAZs) and networks consisting of high functional-class roads only. Consequently, this conventional modeling framework yielded a limited number of link traffic assignments with fairly high estimation errors. In the light of these facts and the obvious need of accurate network-wide traffic estimates, this review is conducted. In particular, this paper provides an extensive review of using traditional travel demand models for improved network-‐wide traffic volume estimation. The paper then focuses on the challenges and opportunities in achieving high-fidelity travel demand model (HFTDM). This review has revealed that, opportunities in relation to both technological advances and intelligent data present a substantial potential in developing the proposed HFTDM for a much more accurate traffic estimation at a network-‐wide level. Finally, the paper concludes with key findings from the review and provides a few recommendations for future research related to the topic.
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Luo, Sida. "Departure and travel time model for the temporal distribution of morning rush-hour traffic congestion." International Journal of Modern Physics C 31, no. 02 (December 30, 2019): 2050023. http://dx.doi.org/10.1142/s0129183120500230.
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The chronic traffic congestion undermines the level of satisfaction within a society. This study proposes a departure time model for estimating the temporal distribution of morning rush-hour traffic congestion over urban road networks. The departure time model is developed based on the point queue model that is used for estimating travel time. First, we prove the effectiveness of the travel time model (i.e. point queue), showing that it gives the same travel time estimation as the kinematic wave model does for a road with successive bottlenecks. Then, a variant of the bottleneck model is developed accordingly, aiming to capture travelers’ departure time choice for commute trips. The proposed departure time model relaxes a traditional assumption that the last commuter experiences the free flow travel time and considers travelers’ unwillingness of late arrivals for work. Numerical experiments show that the morning rush-hour generally starts at 7:29 am and ends at 8:46 am with a traffic congestion delay index (TCDI) of 2.164 for Beijing, China. Furthermore, the estimation of rush-hour start and end time is insensitive to most model parameters including the proportion of travelers who tend to arrive at work earlier than their schedules.
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Genser, Alexander, Noel Hautle, Michail Makridis, and Anastasios Kouvelas. "An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation." Sensors 22, no. 1 (December 26, 2021): 144. http://dx.doi.org/10.3390/s22010144.
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A reliable estimation of the traffic state in a network is essential, as it is the input of any traffic management strategy. The idea of using the same type of sensors along large networks is not feasible; as a result, data fusion from different sources for the same location should be performed. However, the problem of estimating the traffic state alongside combining input data from multiple sensors is complex for several reasons, such as variable specifications per sensor type, different noise levels, and heterogeneous data inputs. To assess sensor accuracy and propose a fusion methodology, we organized a video measurement campaign in an urban test area in Zurich, Switzerland. The work focuses on capturing traffic conditions regarding traffic flows and travel times. The video measurements are processed (a) manually for ground truth and (b) with an algorithm for license plate recognition. Additional processing of data from established thermal imaging cameras and the Google Distance Matrix allows for evaluating the various sensors’ accuracy and robustness. Finally, we propose an estimation baseline MLR (multiple linear regression) model (5% of ground truth) that is compared to a final MLR model that fuses the 5% sample with conventional loop detector and traffic signal data. The comparison results with the ground truth demonstrate the efficiency and robustness of the proposed assessment and estimation methodology.
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Xia, HanQing, XiaoMing Liu, ZeChao Ma, Fang Zhu, Lin Zhang, YingJie Zhao, and YuanRong Wang. "Vehicle Speed and Position Estimation considering Microscopic Heterogeneous Car-Following Characteristics in Connected Vehicle Environments." Journal of Advanced Transportation 2023 (December 8, 2023): 1–21. http://dx.doi.org/10.1155/2023/6627042.
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This paper proposes a method for estimating the speed and position of unsampled vehicles using sampled data from connected automated vehicles (CAVs). The determination of vehicle speed and position on the road is a challenging and crucial task, as they can effectively reflect traffic flow characteristics and contribute to traffic state estimation and intersection signal timing optimization. Connected automated vehicles have the capability to upload their own trajectory data while also capturing trajectory data of surrounding vehicles through onboard sensors. Therefore, this paper proposes a novel approach to estimate the speed and position of unsampled vehicles. Firstly, using real vehicle trajectory data, the correlation between the velocity of following vehicles and the velocity of leading vehicles under different densities is analyzed, leading to the development of a velocity estimation model incorporating a speed correction factor. Secondly, the correlation between time headway, the rate of change of following vehicle acceleration, and traffic density is examined. To address the issue of heterogeneous behavior in vehicle following described by the Intelligent Driver Model (IDM), a real-time optimization model for estimating vehicle position by optimizing IDM parameters is proposed. The velocity estimation model and the position estimation model are summarized as two nonlinear optimization problems. Finally, the proposed method is validated using actual vehicle trajectory data. Experimental results demonstrate that when the number of connected automated vehicles (CAVs) is 2, the proposed method reduces the average absolute error by 30.73% and the standard deviation of the average absolute error by 42.8% compared to a linear model-based speed estimation method under different density conditions. Compared to a method that estimates vehicle position by calibrating desired gaps, the proposed method reduces the average absolute error by 38.2% and the standard deviation of the average absolute error by 41.7% under different density conditions. Furthermore, the proposed method exhibits good practicality under different CAV penetration rates.
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Keler, Andreas, Andreas Kunz, Sasan Amini, and Klaus Bogenberger. "Calibration of a Microscopic Traffic Simulation in an Urban Scenario Using Loop Detector Data." SUMO Conference Proceedings 4 (June 29, 2023): 153. http://dx.doi.org/10.52825/scp.v4i.223.
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Travel demand is an essential input for the creation of traffic models. However, estimating travel demand to accurately represent traffic behaviour usually requires the collection of extensive sets of data on traffic behaviour. Traffic counts are a comparably cost effective and reproducible source of information on travel demand. The utilisation of traffic counts to estimate demand is commonly found in the literature as the static and dynamic O-D estimation problem. A variety of approaches have been developed over recent decades to tackle this problem. Usually initial estimates of the O-D matrix are calibrated by utilising traffic counts and considering different assignment models. Other approaches for the estimation of travel demand solely based on traffic measurements can be found in the simulation software SUMO. The present work demonstrates the systematic development of a network model in SUMO in the inner city of Munich. In a sample network the estimation of travel demand through the tools flowrouter and routeSampler is tested by utilising flow measurements from induction loop detectors. The tests delivered unsatisfactory results, which is proven through observations of traffic flows in the resulting simulations as well as comparisons to historic traffic counts. The lack of sufficient detector data and the complexity of the sample network are discussed as the main reasons for the results. It is concluded that the applied tools should be tested in future studies with a more extensive dataset to perform a more comprehensive review of both tools. Therefore, we deliver specific requirements based on the network example of Munich.
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Gu, Yiming, Zhen (Sean) Qian, and Guohui Zhang. "Traffic State Estimation for Urban Road Networks Using a Link Queue Model." Transportation Research Record: Journal of the Transportation Research Board 2623, no. 1 (January 2017): 29–39. http://dx.doi.org/10.3141/2623-04.
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Traffic state estimation (TSE) is used for real-time estimation of the traffic characteristics (such as flow rate, flow speed, and flow density) of each link in a transportation network, provided with sparse observations. The complex urban road dynamics and flow entry and exit on urban roads challenge the application of TSE on large-scale urban road networks. Because of increasingly available data from various sources, such as cell phones, GPS, probe vehicles, and inductive loops, a theoretical framework is needed to fuse all data to best estimate traffic states in large-scale urban networks. In this context, a Bayesian probabilistic model to estimate traffic states is proposed, along with an expectation–maximization extended Kalman filter (EM-EKF) algorithm. The model incorporates a mesoscopic traffic flow propagation model (the link queue model) that can be computationally efficient for large-scale networks. The Bayesian framework can seamlessly integrate multiple data sources for best inferring flow propagation and flow entry and exit along roads. A synthetic test bed was created. The experiments show that the EM-EKF algorithm can promptly estimate traffic states. Another advantage is that the EM-EKF can update its model parameters in real time to adapt to unknown traffic incidents, such as lane closures. Finally, the proposed methodology was applied to estimating travel speed for an urban network in the Washington, D.C., area and resulted in satisfactory estimation results with an 8.5% error rate.
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Bandeira, Jorge M., Pavlos Tafidis, Eloísa Macedo, João Teixeira, Behnam Bahmankhah, Cláudio Guarnaccia, and Margarida C. Coelho. "Exploring the Potential of Web Based Information of Business Popularity for Supporting Sustainable Traffic Management." Transport and Telecommunication Journal 21, no. 1 (February 1, 2020): 47–60. http://dx.doi.org/10.2478/ttj-2020-0004.
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AbstractThis paper explores the potential of using crowdsourcing tools, namely Google “Popular times” (GPT) as an alternative source of information to predict traffic-related impacts. Using linear regression models, we examined the relationships between GPT and traffic volumes, travel times, pollutant emissions and noise of different areas in different periods. Different data sets were collected: i) crowdsourcing information from Google Maps; ii) traffic dynamics with the use of a probe car equipped with a Global Navigation Satellite System data logger; and iii) traffic volumes. The emissions estimation was based on the Vehicle Specific Power methodology, while noise estimations were conducted with the use of “The Common Noise Assessment Methods in Europe” (CNOSSOS-EU) model. This study shows encouraging results, as it was possible to establish clear relationships between GPT and traffic and environmental performance.
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Yu, Wencheng, Ji-Cheng Jang, Yun Zhu, Jianxin Peng, Wenwei Yang, and Kunjie Li. "Enhanced Estimation of Traffic Noise Levels Using Minute-Level Traffic Flow Data through Convolutional Neural Network." Sustainability 16, no. 14 (July 17, 2024): 6088. http://dx.doi.org/10.3390/su16146088.
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The advent of high-resolution minute-level traffic flow data from video surveillance on roads has opened up new opportunities for enhancing the estimation of traffic noise levels. In this study, we propose an innovative method that utilizes time series traffic flow data (TSTFD) to estimate traffic noise levels using a deep learning Convolutional Neural Network (CNN). Unlike traditional traffic flow data, TSTFD offer a unique structure and composition suitable for multidimensional data analysis. Our method was evaluated in a pilot study conducted in Foshan City, China, utilizing traffic flow information obtained from roadside video surveillance systems. Our results indicated that the CNN-based model surpassed traditional data-driven statistical models in estimating traffic noise levels, achieving a reduction in mean squared error (MSE) by 10.16%, mean absolute error (MAE) by 4.48%, and an improvement in the coefficient of determination (R²) by 1.73%. The model demonstrated robust generalization capabilities throughout the test period, exhibiting mean errors ranging from 0.790 to 1.007 dBA. However, the model’s applicability is constrained by the acoustic propagation environment, demonstrating effectiveness on roads with similar surroundings while showing limited applicability to those with different surroundings. Overall, this method is cost-effective and offers enhanced accuracy for the estimation of traffic noise level.
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Gallego, Jean-Louis, Jean-Loup Farges, and Jean-Jacques Henry. "Traffic queue estimation." Top 5, no. 1 (June 1997): 81–93. http://dx.doi.org/10.1007/bf02568531.
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Medina, A., N. Taft, K. Salamatian, S. Bhattacharyya, and C. Diot. "Traffic matrix estimation." ACM SIGCOMM Computer Communication Review 32, no. 4 (October 2002): 161–74. http://dx.doi.org/10.1145/964725.633041.
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Kwon, Jaimyoung, and Pravin Varaiya. "Real-Time Estimation of Origin–Destination Matrices with Partial Trajectories from Electronic Toll Collection Tag Data." Transportation Research Record: Journal of the Transportation Research Board 1923, no. 1 (January 2005): 119–26. http://dx.doi.org/10.1177/0361198105192300113.
Full textAbstract:
The origin–destination (O-D) matrix of a traffic network is usually estimated from link traffic counts combined with a sample survey. Partially observed vehicle trajectories obtained with vehicle reidentification or automatic vehicle identification techniques such as electronic tags provide a new data source for real-time O-D matrix estimation. However, because of incomplete sampling, accurate estimation of O-D matrices from these data is not trivial. A statistical model was developed for such data, and an unbiased estimator of the O-D matrix was derived based on the method of moments. With further exploitation of the sound statistical model, the bootstrap standard error estimate of the O-D matrix estimator was also developed. The algorithm can be computed quickly and performs well under simulation compared with simpler estimators. Applied to data from vehicles with electronic toll collection tags in the San Francisco Bay Area, the algorithm produces a realistic time series of the hourly O-D matrix. The relationship of the proposed estimator with similar methods in the literature was also studied and extension of the methods to general, more complex networks is discussed.
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Ulrich, Veit, Josephine Brückner, Michael Schultz, Sanam Noreen Vardag, Christina Ludwig, Johannes Fürle, Mohammed Zia, Sven Lautenbach, and Alexander Zipf. "Private Vehicles Greenhouse Gas Emission Estimation at Street Level for Berlin Based on Open Data." ISPRS International Journal of Geo-Information 12, no. 4 (March 24, 2023): 138. http://dx.doi.org/10.3390/ijgi12040138.
Full textAbstract:
As one of the major greenhouse gas (GHG) emitters that has not seen significant emission reductions in the previous decades, the transportation sector requires special attention from policymakers. Policy decisions, thereby need to be supported by traffic emission assessments. Estimations of traffic emissions often rely on huge amounts of actual traffic data whose availability is limited, hampering the transferability of the estimation approaches in time and space. Here, we propose a high-resolution estimation of traffic emissions, which is based entirely on open data, such as the road network and points of interest derived from OpenStreetMap (OSM). We estimated the annual average daily GHG emissions from individual motor traffic for the OSM road network in Berlin by combining the estimated Annual Average Daily Traffic Volume (AADTV) with respective emission factors. The AADTV was calculated by simulating car trips with the open routing engine Openrouteservice, weighted by activity functions based on statistics of the German Mobility Panel. Our estimated total annual GHG emissions were 7.3 million t CO2 equivalent. The highest emissions were estimated for the motorways and major roads connecting the city center with the outskirts. The application of the approach to Berlin showed that the method could reflect the traffic pattern. As the input data is freely available, the approach can be applied to other study areas within Germany with little additional effort.