Relevant bibliographies by topics / Travel time (Traffic engineering) Traffic assignment

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Travel time (Traffic engineering) Traffic assignment'

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Published: 4 June 2021
Last updated: 3 February 2022

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Journal articles on the topic "Travel time (Traffic engineering) Traffic assignment"

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Temelcan, Gizem, Hale Gonce Kocken, and Inci Albayrak. "System Optimum Fuzzy Traffic Assignment Problem." PROMET - Traffic&Transportation 31, no. 6 (December 16, 2019): 611–20. http://dx.doi.org/10.7307/ptt.v31i6.3210.

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This paper focuses on converting the system optimum traffic assignment problem (SO-TAP) to system optimum fuzzy traffic assignment problem (SO-FTAP). The SO-TAP aims to minimize the total system travel time on road network between the specified origin and destination points. Link travel time is taken as a linear function of fuzzy link flow; thus each link travel time is constructed as a triangular fuzzy number. The objective function is expressed in terms of link flows and link travel times in a non-linear form while satisfying the flow conservation constraints. The parameters of the problem are path lengths, number of lanes, average speed of a vehicle, vehicle length, clearance, spacing, link capacity and free flow travel time. Considering a road network, the path lengths and number of lanes are taken as crisp numbers. The average speed of a vehicle and vehicle length are imprecise in nature, so these are taken as triangular fuzzy numbers. Since the remaining parameters, that are clearance, spacing, link capacity and free flow travel time are determined by the average speed of a vehicle and vehicle length, they will be triangular fuzzy numbers. Finally, the original SO-TAP is converted to a fuzzy quadratic programming (FQP) problem, and it is solved using an existing approach from literature. A numerical experiment is illustrated.
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Liu, Henry X., Xuegang Ban, Bin Ran, and Pitu Mirchandani. "Formulation and Solution Algorithm for Fuzzy Dynamic Traffic Assignment Model." Transportation Research Record: Journal of the Transportation Research Board 1854, no. 1 (January 2003): 114–23. http://dx.doi.org/10.3141/1854-13.

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An issue that is always important in the development of traffic assignment models is how travelers' perceptions of travel time should be modeled. Because travelers rarely have perfect knowledge of the road network or of the travel conditions, they choose routes on the basis of their perceived travel times. Traditionally, travelers' perceived travel times are treated as random variables, leading to the stochastic traffic assignment problem. However, uncertain factors are also observed in the subjective recognition of travel times by travelers, and these can be illustrated as fuzzy variables. Therefore, a fuzzy dynamic traffic assignment model that takes into account the imprecision and the uncertainties in the route choice process is proposed. By modeling the expressions of perceived travel times as fuzzy variables, this model makes possible the description of a traveler's process of choosing a route that is more accurate and realistic than those from its deterministic or stochastic counter parts. The fuzzy perceived link travel time and fuzzy perceived path travel time are defined, and a fuzzy shortest path algorithm is used to find the group of fuzzy shortest paths and to assign traffic to each of them by using the so-called C-logit method. The results of the proposed model are also compared with those from the stochastic dynamic traffic assignment model, and it is demonstrated that the impact of advanced traveler information systems on the traveler's route choice process can be readily incorporated into the proposed model.
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van der Gun, Jeroen P. T., Adam J. Pel, and Bart van Arem. "Travel times in quasi-dynamic traffic assignment." Transportmetrica A: Transport Science 16, no. 3 (January 1, 2020): 865–91. http://dx.doi.org/10.1080/23249935.2020.1720862.

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Zhang, Kuilin, Hani S. Mahmassani, and Chung-Cheng Lu. "Probit-Based Time-Dependent Stochastic User Equilibrium Traffic Assignment Model." Transportation Research Record: Journal of the Transportation Research Board 2085, no. 1 (January 2008): 86–94. http://dx.doi.org/10.3141/2085-10.

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This study presents a time-dependent stochastic user equilibrium (TDSUE) traffic assignment model within a probit-based path choice decision framework that explicitly takes into account temporal and spatial correlation (traveler interactions) in travel disutilities across a set of paths. The TDSUE problem, which aims to find time-dependent SUE path flows, is formulated as a fixed-point problem and solved by a simulation-based method of successive averages algorithm. A mesoscopic traffic simulator is employed to determine (experienced) time-dependent travel disutilities. A time-dependent shortest-path algorithm is applied to generate new paths and augment a grand path set. Two vehicle-based implementation techniques are proposed and compared in order to show their impact on solution quality and computational efficiency. One uses the classical Monte Carlo simulation approach to explicitly compute path choice probabilities, and the other determines probabilities by sampling vehicles’ path travel costs from an assumed perception error distribution (also using a Monte Carlo simulation process). Moreover, two types of variance-covariance error structures are discussed: one considers temporal and spatial path choice correlation (due to path overlapping) in terms of aggregated path travel times, and the other uses experienced (or empirical) path travel times from a sample of individual vehicle trajectories. A set of numerical experiments are conducted to investigate the convergence pattern of the solution algorithms and to examine the impact of temporal and spatial correlation on path choice behavior.
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Li, Qing Yin, Rui Tang, and Zhang Lu Tan. "Based on Transcad of the Shortest Path Assignment Method." Advanced Materials Research 219-220 (March 2011): 1105–8. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.1105.

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Based on the Transcad all-or-nothing assignment model is a kind of static method; do not consider the travel time will be affected by traffic flow. The insufficient is that it does not conform to reality. In order to solve the all-or-nothing assignment model that to putting all of traffic flow on the shortest path, the text through the defining of the effective path and the traffic flow of the effective path to improve all-or-nothing assignment model. So the other road traffic flow can also assign and the results can reflect the assignment of urban traffic directly. It can be used to study the dynamic traffic assignment and traffic simulation analysis.
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Di, Shan, Changxuan Pan, and Bin Ran. "Stochastic Multiclass Traffic Assignment with Consideration of Risk-Taking Behaviors." Transportation Research Record: Journal of the Transportation Research Board 2085, no. 1 (January 2008): 111–23. http://dx.doi.org/10.3141/2085-13.

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A study of the problem of predicting traffic flows under traffic equilibrium in a stochastic transportation network is presented. Travelers’ risk-taking behaviors are explicitly modeled with respect to probabilistic travel times. Traveling risks are quantified from the travel time distributions directly and are embedded in the route choice conditions. The classification of risk-neutral, risk-averse, and risk-prone travelers is based on their preferred traveling risks. The formulation of the model clarifies that travelers with different risk preferences have the same objective–to save travel time cost–though they may make different route choices. The proposed solution algorithm is applicable for networks with normal distribution link travel times theoretically. Further simulation analysis shows that it can also be applied to approximate the equilibrium network flows for other frequently used travel time distribution families: gamma, Weibull, and log-normal. The proposed model was applied to a test network and a medium-sized transportation network. The results demonstrate that the model captures travelers’ risk-taking behaviors more realistically and flexibly compared with existing stochastic traffic equilibrium models.
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Shao, Hu, William H. K. Lam, Qiang Meng, and Mei Lam Tam. "Demand-Driven Traffic Assignment Problem Based on Travel Time Reliability." Transportation Research Record: Journal of the Transportation Research Board 1985, no. 1 (January 2006): 220–30. http://dx.doi.org/10.1177/0361198106198500124.

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Chen, Rongsheng, and Michael W. Levin. "Dynamic User Equilibrium of Mobility-on-Demand System with Linear Programming Rebalancing Strategy." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 1 (January 2019): 447–59. http://dx.doi.org/10.1177/0361198118821629.

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Mobility-on-demand (MoD) services are provided by multiple competing companies. In their competition for travelers, they need to provide minimum travel costs, or travelers will switch to competitors. This study developed a dynamic traffic assignment of MoD systems. A static traffic assignment (STA) model is first defined. When demand is asymmetric, empty rebalancing trips are required to move vehicles to traveler origins, and the optimal rebalancing flows are found by a linear program. Because of the time-dependent nature of traveler demand, the model was converted to dynamic traffic assignment (DTA). The method of successive averages, which is provably convergent for STA, was used to find dynamic user equilibrium (DUE). The simulation was conducted on two networks. The MoD system was simulated with different fleet sizes and demands. The results showed that the average total delay and travel distance decreased with the increase in fleet size whereas the average on-road travel time increased with the fleet size. The result of traffic assignment of one network with MoD system was compared with a network where all travelers use private vehicles. The results showed that the network with MoD system created more trips but less traffic congestion.
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Lu, Qiong, and Tamás Tettamanti. "Traffic Control Scheme for Social Optimum Traffic Assignment with Dynamic Route Pricing for Automated Vehicles." Periodica Polytechnica Transportation Engineering 49, no. 3 (September 1, 2021): 301–7. http://dx.doi.org/10.3311/pptr.18608.

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In transportation modeling, after defining a road network and its origin-destination (OD) matrix, the next important question is how to assign traffic among OD-pairs. Nowadays, advanced traveler information systems (ATIS) make it possible to realize the user equilibrium solution. Simultaneously, with the advent of the Cooperative Intelligent Transport Systems (C-ITS), it is possible to solve the traffic assignment problem in a system optimum way. As a potential traffic assignment method in the future transportation system for automated cars, the deterministic system optimum (DSO) is modeled and simulated to investigate the potential changes it may bring to the existing traditional traffic system. In this paper, stochastic user equilibrium (SUE) is used to simulate the conventional traffic assignment method. This work concluded that DSO has considerable advantages in reducing trip duration, time loss, waiting time, and departure delay under the same travel demand. What is more, the SUE traffic assignment has a more dispersed vehicle density distribution. Moreover, DSO traffic assignment helps the maximum vehicle density of each alternative path arrive almost simultaneously. Furthermore, DSO can significantly reduce or avoid the occurrence of excessive congestion.
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Kamel, Islam, Amer Shalaby, and Baher Abdulhai. "Integrated simulation-based dynamic traffic and transit assignment model for large-scale network." Canadian Journal of Civil Engineering 47, no. 8 (August 2020): 898–907. http://dx.doi.org/10.1139/cjce-2018-0706.

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Although the traffic and transit assignment processes are intertwined, the interactions between them are usually ignored in practice, especially for large-scale networks. In this paper, we build a simulation-based traffic and transit assignment model that preserves the interactions between the two assignment processes for the large-scale network of the Greater Toronto Area during the morning peak. This traffic assignment model is dynamic, user-equilibrium seeking, and includes surface transit routes. It utilizes the congested travel times, determined by the dynamic traffic assignment, rather than using predefined timetables. Unlike the static transit assignment models, the proposed transit model distinguishes between different intervals within the morning peak by using the accurate demand, transit schedule, and time-based road level-of-service. The traffic and transit assignment models are calibrated against actual field observations. The resulting dynamic model is suitable for testing different demand management strategies that impose dynamic changes on multiple modes simultaneously.
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Dissertations / Theses on the topic "Travel time (Traffic engineering) Traffic assignment"

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Lu, Chenxi. "Improving Analytical Travel Time Estimation for Transportation Planning Models." FIU Digital Commons, 2010. http://digitalcommons.fiu.edu/etd/237.

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This dissertation aimed to improve travel time estimation for the purpose of transportation planning by developing a travel time estimation method that incorporates the effects of signal timing plans, which were difficult to consider in planning models. For this purpose, an analytical model has been developed. The model parameters were calibrated based on data from CORSIM microscopic simulation, with signal timing plans optimized using the TRANSYT-7F software. Independent variables in the model are link length, free-flow speed, and traffic volumes from the competing turning movements. The developed model has three advantages compared to traditional link-based or node-based models. First, the model considers the influence of signal timing plans for a variety of traffic volume combinations without requiring signal timing information as input. Second, the model describes the non-uniform spatial distribution of delay along a link, this being able to estimate the impacts of queues at different upstream locations of an intersection and attribute delays to a subject link and upstream link. Third, the model shows promise of improving the accuracy of travel time prediction. The mean absolute percentage error (MAPE) of the model is 13% for a set of field data from Minnesota Department of Transportation (MDOT); this is close to the MAPE of uniform delay in the HCM 2000 method (11%). The HCM is the industrial accepted analytical model in the existing literature, but it requires signal timing information as input for calculating delays. The developed model also outperforms the HCM 2000 method for a set of Miami-Dade County data that represent congested traffic conditions, with a MAPE of 29%, compared to 31% of the HCM 2000 method. The advantages of the proposed model make it feasible for application to a large network without the burden of signal timing input, while improving the accuracy of travel time estimation. An assignment model with the developed travel time estimation method has been implemented in a South Florida planning model, which improved assignment results.
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Zhang, Xu. "INCORPORATING TRAVEL TIME RELIABILITY INTO TRANSPORTATION NETWORK MODELING." UKnowledge, 2017. http://uknowledge.uky.edu/ce_etds/54.

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Travel time reliability is deemed as one of the most important factors affecting travelers’ route choice decisions. However, existing practices mostly consider average travel time only. This dissertation establishes a methodology framework to overcome such limitation. Semi-standard deviation is first proposed as the measure of reliability to quantify the risk under uncertain conditions on the network. This measure only accounts for travel times that exceed certain pre-specified benchmark, which offers a better behavioral interpretation and theoretical foundation than some currently used measures such as standard deviation and the probability of on-time arrival. Two path finding models are then developed by integrating both average travel time and semi-standard deviation. The single objective model tries to minimize the weighted sum of average travel time and semi-standard deviation, while the multi-objective model treats them as separate objectives and seeks to minimize them simultaneously. The multi-objective formulation is preferred to the single objective model, because it eliminates the need for prior knowledge of reliability ratios. It offers an additional benefit of providing multiple attractive paths for traveler’s further decision making. The sampling based approach using archived travel time data is applied to derive the path semi-standard deviation. The approach provides a nice workaround to the problem that there is no exact solution to analytically derive the measure. Through this process, the correlation structure can be implicitly accounted for while simultaneously avoiding the complicated link travel time distribution fitting and convolution process. Furthermore, the metaheuristic algorithm and stochastic dominance based approach are adapted to solve the proposed models. Both approaches address the issue where classical shortest path algorithms are not applicable due to non-additive semi-standard deviation. However, the stochastic dominance based approach is preferred because it is more computationally efficient and can always find the true optimal paths. In addition to semi-standard deviation, on-time arrival probability and scheduling delay measures are also investigated. Although these three measures share similar mathematical structures, they exhibit different behaviors in response to large deviations from the pre-specified travel time benchmark. Theoretical connections between these measures and the first three stochastic dominance rules are also established. This enables us to incorporate on-time arrival probability and scheduling delay measures into the methodology framework as well.
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Chin, Kian Keong. "Departure time choice in equilibrium traffic assignment." Thesis, University of Leeds, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364638.

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Hodges, Fiona. "Travel time budgets in an urban area /." Connect to thesis, 1994. http://eprints.unimelb.edu.au/archive/00000227.

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Abdelfatah, Akmal Saad. "Time-dependent signal control and system optimal traffic assignment in congested vehicular traffic networks /." Digital version accessible at:, 1999. http://wwwlib.umi.com/cr/utexas/main.

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Chan, Ping-ching Winnie. "The value of travel time savings in Hong Kong." Hong Kong : University of Hong Kong, 2000. http://sunzi.lib.hku.hk:8888/cgi-bin/hkuto%5Ftoc%5Fpdf?B23425003.

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Misra, Rajul. "Toward a comprehensive representation and analysis framework for non-worker activity-travel pattern modeling /." Digital version accessible at:, 1999. http://wwwlib.umi.com/cr/utexas/main.

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Wu, Seung Kook. "Adaptive traffic control effect on arterial travel time charateristics." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31839.

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Thesis (Ph.D)--Civil and Environmental Engineering, Georgia Institute of Technology, 2010.
Committee Chair: Hunter, Michael; Committee Member: Guensler, Randall; Committee Member: Leonard, John; Committee Member: Rodgers, Michael; Committee Member: Roshan J. Vengazhiyil. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Gao, Song 1976. "Optimal adaptive routing and traffic assignment in stochastic time-dependent networks." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/30188.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2005.
Includes bibliographical references (p. 233-237).
A stochastic time-dependent (STD) network is defined by treating all link travel times at all time periods as random variables, with possible time-wise and link-wise stochastic dependency. A routing policy is a decision rule which specifies what node to take next out of the current node based on the current time and online information. A formal framework is established for optimal routing policy problems in STD networks, including generic optimality conditions, and a comprehensive taxonomy with insights into variants of the problem. A variant pertinent to road traffic networks is studied in detail, where a discrete joint distribution of link travel times is used to accommodate the most general stochastic dependency among link travel times, and the access to perfect online information about link travel times is assumed. Both exact and approximation solution algorithms are designed and tested. The criteria of optimality are then extended to reliability measures, such as travel time variance and expected early/late schedule delays. The first routing-policy-based stochastic dynamic traffic assignment (DTA) model is established. A general framework is provided and the equilibrium problem is formulated as a fixed point problem with three components: the optimal routing policy generation module, the routing policy choice model and the policy-based dynamic network loader. An MSA (method of successive averages) heuristic is designed. Computational tests are carried out in a. hypothetical network, where random incidents are the source of stochasticity. The heuristic converges satisfactorily in the test network under the proposed test settings. The' adaptiveness in the routing policy based model leads to travel time savings at equilibrium.
(cont.) As a byproduct, travel time reliability is also enhanced. The value of online information is an increasing function of the incident probability. Travel time savings are high when market penetrations are low. However, the function of travel time saving against market penetration is not monotonic. This suggests that in a travelers' information system or route guidance system, the information penetration needs to be chosen carefully to maximize benefits.
by Song Gao.
Ph.D.
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Farver, Jennifer M. (Jennifer Margaret) 1976. "Continuous time algorithms for a variant of the dynamic traffic assignment problem." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/84247.

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Books on the topic "Travel time (Traffic engineering) Traffic assignment"

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Kittelson & Associates. Evaluating alternative operations strategies to improve travel time reliability. Washington, D.C: Transportation Research Board, 2013.

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Second Strategic Highway Research Program (U.S.), Kimley-Horn and Associates, and Parsons Brinckerhoff, eds. Integrating business processes to improve travel time reliability. Washington, D.C: Transportation Research Board, 2011.

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Second Strategic Highway Research Program (U.S.), Kimley-Horn and Associates, and Parsons Brinckerhoff, eds. Guide to integrating business processes to improve travel time reliability. Washington, D.C: Transportation Research Board, 2011.

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Small, Kenneth A. Valuation of travel-time savings and predictability in congested conditions for highway user-cost estimation. Washington, D.C: National Academy Press, 1999.

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United States. Federal Highway Administration., ed. Analysis of national and regional travel trends. Washington, D.C: Dept. of Transportation, Federal Highway Administration, 1986.

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Arnold, E. D. Changes in travel in the Shirley Highway corridor, 1983-1986. Charlottesville, Va: Virginia Transportation Research Council, in cooperation with the U.S. Dept. of Transportation, Federal Highway Administration, 1987.

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Hyŏn, Pak. Yebi tʻadangsŏng chosa chaengchŏm yŏnʼgu. Sŏul Tʻŭkpyŏlsi: Hanʼguk Kaebal Yŏnʼguwŏn, 2006.

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North, American Travel Monitoring Exhibition and Conference (2000 Middleton Wis ). North American Travel Monitoring Exhibition and Conference, TRB Data Committee's mid-year meetings, August 27-31, 2000, Middleton, WI. Madison, Wis: Wisconsin Dept. of Transportation, 2000.

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Arnold, E. D. Congestion on Virginia's urban highways. Charlottesville, Va: Virginia Transportation Research Council, in cooperation with the U.S. Dept. of Transportation, Federal Highway Administration, 1988.

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Lau, Lorrie A. Y. Analysis of national and regional travel trends. Washington, D.C: Dept. of Transportation, Federal Highway Administration, 1986.

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Book chapters on the topic "Travel time (Traffic engineering) Traffic assignment"

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Amrutsamanvar, Rushikesh, Gaurang Joshi, Shriniwas S. Arkatkar, and Ravi Sekhar Chalumuri. "Empirical Travel Time Reliability Assessment of Indian Urban Roads." In Recent Advances in Traffic Engineering, 165–82. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3742-4_11.

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Prajapati, N. I., A. K. Sutariya, and H. R. Varia. "Travel Time Delay Study on Congested Urban Road Links of Ahmedabad City." In Recent Advances in Traffic Engineering, 121–37. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3742-4_8.

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Saw, Krishna, Bhimaji K. Katti, and Gaurang J. Joshi. "Fuzzy Rule-Based Travel Time Estimation Modelling: A Case Study of Surat City Traffic Corridor." In Recent Advances in Traffic Engineering, 183–98. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3742-4_12.

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Wei, Chong, Yasuo Asakura, and Takamasa Iryo. "A Link-Based Stochastic Traffic Assignment Model for Travel Time Reliability Estimation." In Transportation Research, Economics and Policy, 209–21. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0947-2_12.

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Sen, Saptarshi, and Sudip Kumar Roy. "Quantifying Travel Time Reliability of Air-Conditioned Public Buses in Urban Area: A Case Study of Kolkata." In Recent Advances in Traffic Engineering, 403–20. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3742-4_25.

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Ni, Anning, Xuxun Lin, and Jing Luo. "Stochastic Traffic Assignment Model Considering Park & Ride Network and Travel Time Reliability." In Green Intelligent Transportation Systems, 873–86. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3551-7_70.

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Ganapathy, Jayanthi, and Fausto Pedro García Márquez. "Travel Time Based Traffic Rerouting by Augmenting Traffic Flow Network with Temporal and Spatial Relations for Congestion Management." In Proceedings of the Fifteenth International Conference on Management Science and Engineering Management, 554–65. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79203-9_43.

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Deb Nath, Rudra Pratap, Hyun-Jo Lee, Nihad Karim Chowdhury, and Jae-Woo Chang. "Modified K-Means Clustering for Travel Time Prediction Based on Historical Traffic Data." In Knowledge-Based and Intelligent Information and Engineering Systems, 511–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15387-7_55.

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Lawniczak, Anna T., and Bruno N. Di Stefano. "Development of Road Traffic CA Model of 4-Way Intersection to Study Travel Time." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 2040–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02469-6_80.

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Ciskowski, Piotr, Adrianna Janik, Marek Bazan, Krzysztof Halawa, Tomasz Janiczek, and Andrzej Rusiecki. "Estimation of Travel Time in the City Based on Intelligent Transportation System Traffic Data with the Use of Neural Networks." In Dependability Engineering and Complex Systems, 85–95. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39639-2_8.

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Conference papers on the topic "Travel time (Traffic engineering) Traffic assignment"

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Tang, Xiaoyong, Lin Cheng, and Shang Xu. "Consideration of Travel Time Reliability in Traffic Assignment." In Second International Conference on Transportation Engineering. Reston, VA: American Society of Civil Engineers, 2009. http://dx.doi.org/10.1061/41039(345)667.

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Yuan, Zhenzhou. "Study of Link Travel Time Functions for Dynamic Traffic Assignment Models." In International Conference on Traffic and Transportation Studies (ICTTS) 2002. Reston, VA: American Society of Civil Engineers, 2002. http://dx.doi.org/10.1061/40630(255)98.

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Kuang, Aiwu, Zhongxiang Huang, and W. K. Victor Chan. "Stochastic User Equilibrium Traffic Assignment Model Based on Travel Time Budget." In First International Symposium on Transportation and Development Innovative Best Practices. Reston, VA: American Society of Civil Engineers, 2008. http://dx.doi.org/10.1061/40961(319)28.

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Tian, Ye, Yi-Chang Chiu, and Jian Sun. "A Trajectory-Mining Approach to Derive Travel Time Skim Matrix in Dynamic Traffic Assignment." In 19th COTA International Conference of Transportation Professionals. Reston, VA: American Society of Civil Engineers, 2019. http://dx.doi.org/10.1061/9780784482292.381.

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Xu, Tiandong, Yuan Hao, and Lijun Sun. "Travel Time Prediction of Urban Expressway in Unstable Traffic Flow." In First International Conference on Transportation Engineering. Reston, VA: American Society of Civil Engineers, 2007. http://dx.doi.org/10.1061/40932(246)361.

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Li, Fazhi, Lei Xiao, and Ke Wang. "Studying Delivery Travel Time Reliability Based on Traffic Flow Fluctuations." In International Conference of Logistics Engineering and Management (ICLEM) 2010. Reston, VA: American Society of Civil Engineers, 2010. http://dx.doi.org/10.1061/41139(387)185.

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Wang, Chengsong, Hui Fu, and Gang Hu. "Optimal emergency rescue route for traffic accident considering variable travel time." In 2013 6th International Conference on Information Management, Innovation Management and Industrial Engineering (ICIII). IEEE, 2013. http://dx.doi.org/10.1109/iciii.2013.6703187.

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Pop, Madalin-Dorin. "Decision Making in Road Traffic Coordination Methods: A Travel Time Reduction Perspective." In 2020 International Conference Engineering Technologies and Computer Science (EnT). IEEE, 2020. http://dx.doi.org/10.1109/ent48576.2020.00014.

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Zhang, Mingbo, Bin Zeng, and Ziruo Yu. "Study on the method of travel time estimation in urban road traffic flow." In 2011 International Conference on Electric Technology and Civil Engineering (ICETCE). IEEE, 2011. http://dx.doi.org/10.1109/icetce.2011.5776164.

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Larsen, Gustavo Henrique, Leopoldo Rideki Yoshioka, and Claudio Luiz Marte. "Bus Travel Times Prediction based on Real-Time Traffic Data Forecast using Artificial Neural Networks." In 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE). IEEE, 2020. http://dx.doi.org/10.1109/icecce49384.2020.9179382.

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