Relevant bibliographies by topics / Travel time (Traffic estimation)

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  1. Journal articles

Academic literature on the topic 'Travel time (Traffic estimation)'

Author: Grafiati
Published: 29 July 2024
Last updated: 31 July 2024

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Journal articles on the topic "Travel time (Traffic estimation)"

1

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 (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
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2

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 (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 tra
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3

Ji, Yuxiong, Shengchuan Jiang, Yuchuan Du, and H. Michael Zhang. "Estimation of Bimodal Urban Link Travel Time Distribution and Its Applications in Traffic Analysis." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/615468.

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Vehicles travelling on urban streets are heavily influenced by traffic signal controls, pedestrian crossings, and conflicting traffic from cross streets, which would result in bimodal travel time distributions, with one mode corresponding to travels without delays and the other travels with delays. A hierarchical Bayesian bimodal travel time model is proposed to capture the interrupted nature of urban traffic flows. The travel time distributions obtained from the proposed model are then considered to analyze traffic operations and estimate travel time distribution in real time. The advantage o
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4

Xu, Tian-dong, Yuan Hao, Zhong-ren Peng, and Li-jun Sun. "Real-time travel time predictor for route guidance consistent with driver behavior." Canadian Journal of Civil Engineering 39, no. 10 (2012): 1113–24. http://dx.doi.org/10.1139/l2012-092.

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Providing reliable real-time travel time information is a critical challenge to all existing traffic routing systems. This study develops a new model for estimating and predicting real-time traffic conditions and travel times for variable message signs-based route guidance system. The proposed model is based on real-time limited detected traffic data, stochastic nonlinear macroscopic traffic flow model, and adaptive Kalman filtering theory. The method has the following main features: (1) real-time estimation and prediction of traffic conditions on a network level using limited traffic detector
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5

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 (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
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6

Gu, Jian, Miaohua Li, Linghua Yu, Shun Li, and Kejun Long. "Analysis on Link Travel Time Estimation considering Time Headway Based on Urban Road RFID Data." Journal of Advanced Transportation 2021 (April 13, 2021): 1–19. http://dx.doi.org/10.1155/2021/8876626.

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In this paper, the calculation method of the link travel time is firstly analysed in the continuous traffic flow by using the detection data collected when vehicles pass through urban links, and a theoretical derivation formula for estimating link travel time is proposed by considering the typical vehicle travel time and the time headway deviation upstream and downstream of the links as the main parameters. A typical vehicle analysis method based on link travel time similarity is proposed, and the theoretical formula is optimized, respectively. Then, an estimation formula based on maximum trav
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7

Nanthawichit, Chumchoke, Takashi Nakatsuji, and Hironori Suzuki. "Application of Probe-Vehicle Data for Real-Time Traffic-State Estimation and Short-Term Travel-Time Prediction on a Freeway." Transportation Research Record: Journal of the Transportation Research Board 1855, no. 1 (2003): 49–59. http://dx.doi.org/10.3141/1855-06.

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Traffic information from probe vehicles has great potential for improving the estimation accuracy of traffic situations, especially where no traffic detector is installed. A method for dealing with probe data along with conventional detector data to estimate traffic states is proposed. The probe data were integrated into the observation equation of the Kalman filter, in which state equations are represented by a macroscopic traffic-flow model. Estimated states were updated with information from both stationary detectors and probe vehicles. The method was tested under several traffic conditions
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8

Huang, Xiaohui, Pan He, Anand Rangarajan, and Sanjay Ranka. "Machine-Learning-Based Real-Time Multi-Camera Vehicle Tracking and Travel-Time Estimation." Journal of Imaging 8, no. 4 (2022): 101. http://dx.doi.org/10.3390/jimaging8040101.

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Travel-time estimation of traffic flow is an important problem with critical implications for traffic congestion analysis. We developed techniques for using intersection videos to identify vehicle trajectories across multiple cameras and analyze corridor travel time. Our approach consists of (1) multi-object single-camera tracking, (2) vehicle re-identification among different cameras, (3) multi-object multi-camera tracking, and (4) travel-time estimation. We evaluated the proposed framework on real intersections in Florida with pan and fisheye cameras. The experimental results demonstrate the
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9

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 (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 wa
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10

Guo, Yajuan, and Licai Yang. "Reliable Estimation of Urban Link Travel Time Using Multi-Sensor Data Fusion." Information 11, no. 5 (2020): 267. http://dx.doi.org/10.3390/info11050267.

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Travel time is one of the most critical indexes to describe urban traffic operating states. How to obtain accurate and robust travel time estimates, so as to facilitate to make traffic control decision-making for administrators and trip-planning for travelers, is an urgent issue of wide concern. This paper proposes a reliable estimation method of urban link travel time using multi-sensor data fusion. Utilizing the characteristic analysis of each individual traffic sensor data, we first extract link travel time from license plate recognition data, geomagnetic detector data and floating car data
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