Relevant bibliographies by topics / Travel time (Traffic estimation)

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  2. Dissertations / Theses
  3. Books
  4. Book chapters
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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)"

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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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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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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 of the proposed bimodal model is demonstrated using empirical data, and the results are encouraging.
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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 (October 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 detectors, (2) travel time prediction in free flow and congested flow, and (3) prediction of drivers’ en-route diversion behavior. Field testing is conducted based on the Route Guidance Pilot Project sponsored by the National Science and Technology Ministry of China. The achieved testing results are satisfactory and have potential use for future works and field applications.
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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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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 travel time similarity and an estimation formula based on maximum travel time confidence interval similarity are proposed, respectively. Finally, when analysing the fitting conditions, the collected data from urban roads in Nanjing are used to verify the proposed travel time estimation method based on the radio frequency identification devices. The results show that time headway deviation converges to zero when the hourly vehicle volume is more than 20 veh/h in the certain flow direction, and there are more positive and negative fluctuations when the hourly vehicle volume is less than 10 veh/h in the certain flow direction. The accuracy of the proposed improved method based on typical vehicle travel time estimation is significantly improved by considering the typical vehicle travel time, and typical vehicles on the road segment mainly exist at the tail of the traffic platoon in the corresponding period.
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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 (January 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 by using hypothetical data, giving considerably improved estimation results compared to those estimated without probe data. Finally, the application of the proposed method was extended to the estimation and short-term prediction of travel time. Travel times were obtained indirectly through the conversion of speeds estimated or predicted by the proposed method. Experimental results show that the performance of travel-time estimation or prediction is comparable to that of some existing methods.
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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 (April 6, 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 viability and effectiveness of our method.
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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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Guo, Yajuan, and Licai Yang. "Reliable Estimation of Urban Link Travel Time Using Multi-Sensor Data Fusion." Information 11, no. 5 (May 16, 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, respectively, and find that their distribution patterns are similar and follow logarithmic normal distribution. Then, a support degree algorithm based on similarity function and a credibility algorithm based on membership function are developed, aiming to overcome the conflicts among multi-sensor traffic data and the uncertainties of single-sensor traffic data. The reliable fusion weights for each type of traffic sensor data are further determined by integrating the corresponding support degree with credibility. A case study was conducted using real-world data from a link of Jingshi Road in Jinan, China and demonstrated that the proposed method can effectively improve the accuracy and reliability of link travel time estimations in urban road systems.
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Dissertations / Theses on the topic "Travel time (Traffic estimation)"

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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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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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Chan, Ping-ching Winnie, and 陳冰淸. "The value of travel time savings in Hong Kong." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B31954789.

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Respati, Sara Wibawaning. "Network-scale arterial traffic state prediction: Fusing multisensor traffic data." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/202990/1/Sara%20Wibawaning_Respati_Thesis.pdf.

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Road traffic congestion is an increasing societal problem. Road agencies and users seeks accurate and reliable travel speed information. This thesis developed a network-scale traffic state prediction based on Convolutional Neural Network (CNN). The method can predict the speed over the network accurately by preserving road connectivity and incorporating historical datasets. When dealing with an extensive network, the thesis also developed a clustering method to reduce the complexity of the prediction. By accurately predict the traffic state over a network, traffic operators can manage the network more effectively and travellers can make informed decision on their journeys.
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Soriguera, Martí Francesc. "Highway travel time estimation with data fusion." Doctoral thesis, Universitat Politècnica de Catalunya, 2010. http://hdl.handle.net/10803/108910.

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La informació sobre el temps de viatge és l'indicador clau en el funcionament operatiu d’una autopista i un dels inputs més apreciats pels seus usuaris. Tot i això, no ha estat fins molt darrerament que els operadors d'autopistes han començat a analitzar el trànsit de la carretera amb l’objectiu de proporcionar informació acurada sobre el temps de viatge. També recentment, les administracions titulars de les autopistes han començat a demanar que es proporcioni tal informació com a mesura del servei d'accessibilitat proporcionat per la carretera, en termes de qualitat i fiabilitat. Durant el segle passat, els detectors d’espira magnètica jugaven un paper primordial en la monitorització del trànsit al proporcionar informació sobre el volum de trànsit i també sobre la velocitat i longitud mitjanes dels vehicles, encara que generalment amb menys precisió en aquests dos darrers casos. En les darreres dècades han aparegut noves tecnologies de control de trànsit (càmeres intel•ligents, seguiment mitjançant GPS o telèfon mòbil, identificació de dispositius bluetooth, nous detectors MeMS, etc.) que permeten millorar considerablement la recollida de dades sobre el temps de viatge. Algunes d’aquestes tecnologies són barates (bluetooth), d’altres no ho són (càmeres); però en qualsevol cas la major part de la xarxa d’autopistes encara està controlada per detectors d’espira magnètica. Té sentit doncs, emprar la seva informació bàsica i enriquir-la, quan calgui, amb noves fonts de dades. Aquesta tesi presenta una metodologia nova i simple per a la previsió a curt termini del temps de viatge en autopistes de peatge basada en la fusió de dades provinents de detectors d’espira magnètica i de tiquets de peatge. La metodologia és genèrica i no és tecnològicament captiva: es podria generalitzar fàcilment a uns altres tipus de dades. L'anàlisi Bayesià permet obtenir dades fusionades que són més fiables que les dades d’entrada originals, superant alguns problemes habituals en l’estimació del temps de viatge a partir de fonts úniques d’informació. La metodologia desenvolupada aporta valor afegit a les dades actuals (detectors d’espira i tiquets de peatge) en autopistes de peatge tancat, i aprofita al màxim (en termes d'estimació del temps de viatge) les dades disponibles, sense caure en la demanda recurrent i costosa d’una major necessitat de dades. L'aplicació dels algoritmes a l’autopista de peatge AP-7 als voltants de Barcelona demostra empíricament la tesi: és possible desenvolupar un sistema acurat d'informació de temps de viatge, en temps real, en autopistes de peatge tancat amb la monitorització existent. Per això, d'ara en endavant els operadors d'autopistes podran oferir aquest valor afegit als seus clients sense gairebé cap inversió extra.
Travel time information is the key indicator of highway management performance and one of the most appreciated inputs for highway users. Despite this relevance, the interest of highway operators in providing approximate travel time information is quite recent. Besides, highway administrations have also recently begun to request such information as a means to measure the accessibility service provided by the road, in terms of quality and reliability. In the last century, magnetic loop detectors played a role in providing traffic volume information and also, with less accuracy, information on average speed and vehicle length. New traffic monitoring technologies (intelligent cameras, GPS or cell phone tracking, Bluetooth identification, new MeMS detectors, etc.) have appeared in recent decades which permit considerable improvement in travel time data gathering. Some of the new technologies are cheap (Bluetooth), others are not (cameras); but in any case most of the main highways are still monitored by magnetic loop detectors. It makes sense to use their basic information and enrich it, when needed, with new data sources. This thesis presents a new and simple approach for the short term prediction of toll highway travel times based on the fusion of inductive loop detector and toll ticket data. The methodology is generic and it is not technologically captive: it could be easily generalized to other equivalent types of data. Bayesian analysis makes it possible to obtain fused estimates that are more reliable than the original inputs, overcoming some drawbacks of travel time estimations based on unique data sources. The developed methodology adds value and obtains the maximum (in terms of travel time estimation) of the available data, without falling in the recurrent and costly request of additional data needs. The application of the algorithms to empirical testing in AP-7 toll highway in Barcelona proves our thesis that it is possible to develop an accurate real-time travel time information system on closed toll highways with the existing surveillance equipment. Therefore, from now on highway operators can give this added value to their customers at almost no extra investment. Finally, research extensions are suggested, and some of the proposed lines are currently under development.
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Shen, Luou. "Freeway Travel Time Estimation and Prediction Using Dynamic Neural Networks." FIU Digital Commons, 2008. http://digitalcommons.fiu.edu/etd/17.

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Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.
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Yang, Shu, and Shu Yang. "Estimating Freeway Travel Time Reliability for Traffic Operations and Planning." Diss., The University of Arizona, 2016. http://hdl.handle.net/10150/623003.

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Travel time reliability (TTR) has attracted increasing attention in recent years, and is often listed as one of the major roadway performance and service quality measures for both traffic engineers and travelers. Measuring travel time reliability is the first step towards improving travel time reliability, ensuring on-time arrivals, and reducing travel costs. Four components may be primarily considered, including travel time estimation/collection, quantity of travel time selection, probability distribution selection, and TTR measure selection. Travel time is a key transportation performance measure because of its diverse applications and it also serves the foundation of estimating travel time reliability. Various modelling approaches to estimating freeway travel time have been well developed due to widespread installation of intelligent transportation system sensors. However, estimating accurate travel time using existing freeway travel time models is still challenging under congested conditions. Therefore, this study aimed to develop an innovative freeway travel time estimation model based on the General Motors (GM) car-following model. Since the GM model is usually used in a micro-simulation environment, the concepts of virtual leading and virtual following vehicles are proposed to allow the GM model to be used in macro-scale environments using aggregated traffic sensor data. Travel time data collected from three study corridors on I-270 in St. Louis, Missouri was used to verify the estimated travel times produced by the proposed General Motors Travel Time Estimation (GMTTE) model and two existing models, the instantaneous model and the time-slice model. The results showed that the GMTTE model outperformed the two existing models due to lower mean average percentage errors of 1.62% in free-flow conditions and 6.66% in two congested conditions. Overall, the GMTTE model demonstrated its robustness and accuracy for estimating freeway travel times. Most travel time reliability measures are derived directly from continuous probability distributions and applied to the traffic data directly. However, little previous research shows a consensus of probability distribution family selection for travel time reliability. Different probability distribution families could yield different values for the same travel time reliability measure (e.g. standard deviation). It is believe that the specific selection of probability distribution families has few effects on measuring travel time reliability. Therefore, two hypotheses are proposed in hope of accurately measuring travel time reliability. An experiment is designed to prove the two hypotheses. The first hypothesis is proven by conducting the Kolmogorov–Smirnov test and checking log-likelihoods, and Akaike information criterion with a correction for finite sample sizes (AICc) and Bayesian information criterion (BIC) convergences; and the second hypothesis is proven by examining both moment-based and percentile-based travel time reliability measures. The results from the two hypotheses testing suggest that 1) underfitting may cause disagreement in distribution selection, 2) travel time can be precisely fitted using mixture models with higher value of the number of mixture distributions (K), regardless of the distribution family, and 3) the travel time reliability measures are insensitive to the selection of distribution family. Findings of this research allows researchers and practitioners to avoid the work of testing various distributions, and travel time reliability can be more accurately measured using mixture models due to higher value of log-likelihoods. As with travel time collection, the accuracy of the observed travel time and the optimal travel time data quantity should be determined before using the TTR data. The statistical accuracy of TTR measures should be evaluated so that the statistical behavior and belief can be fully understood. More specifically, this issue can be formulated as a question: using a certain amount of travel time data, how accurate is the travel time reliability for a specific freeway corridor, time of day (TOD), and day of week (DOW)? A framework for answering this question has not been proposed in the past. Our study proposes a framework based on bootstrapping to evaluate the accuracy of TTR measures and answer the question. Bootstrapping is a computer-based method for assigning measures of accuracy to multiple types of statistical estimators without requiring a specific probability distribution. Three scenarios representing three traffic flow conditions (free-flow, congestion, and transition) were used to fully understand the accuracy of TTR measures under different traffic conditions. The results of the accuracy measurements primarily showed that: 1) the proposed framework can facilitate assessment of the accuracy of TTR, and 2) stabilization of the TTR measures did not necessarily correspond to statistical accuracy. The findings in our study also suggested that moment-based TTR measures may not be statistically sufficient for measuring freeway TTR. Additionally, our study suggested that 4 or 5 weeks of travel time data is enough for measuring freeway TTR under free-flow conditions, 40 weeks for congested conditions, and 35 weeks for transition conditions. A considerable number of studies have contributed to measuring travel time reliability. Travel time distribution estimation is considered as an important starting input of measuring travel time reliability. Kernel density estimation (KDE) is used to estimate travel time distribution, instead of parametric probability distributions, e.g. Lognormal distribution, the two state models. The Hasofer Lind - Rackwitz Fiessler (HL-RF) algorithm, widely used in the field of reliability engineering, is applied to this work. It is used to compute the reliability index of a system based on its previous performance. The computing procedure for travel time reliability of corridors on a freeway is first introduced. Network travel time reliability is developed afterwards. Given probability distributions estimated by the KDE technique, and an anticipated travel time from travelers, the two equations of the corridor and network travel time reliability can be used to address the question, "How reliable is my perceived travel time?" The definition of travel time reliability is in the sense of "on time performance", and it is conducted inherently from the perspective of travelers. Further, the major advantages of the proposed method are: 1) The proposed method demonstrates an alternative way to estimate travel time distributions when the choice of probability distribution family is still uncertain; 2) the proposed method shows its flexibility for being applied onto different levels of roadways (e.g. individual roadway segment or network). A user-defined anticipated travel time can be input, and travelers can utilize the computed travel time reliability information to plan their trips in advance, in order to better manage trip time, reduce cost, and avoid frustration.
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Xiao, Yan. "Hybrid Approaches to Estimating Freeway Travel Times Using Point Traffic Detector Data." FIU Digital Commons, 2011. http://digitalcommons.fiu.edu/etd/356.

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The accurate and reliable estimation of travel time based on point detector data is needed to support Intelligent Transportation System (ITS) applications. It has been found that the quality of travel time estimation is a function of the method used in the estimation and varies for different traffic conditions. In this study, two hybrid on-line travel time estimation models, and their corresponding off-line methods, were developed to achieve better estimation performance under various traffic conditions, including recurrent congestion and incidents. The first model combines the Mid-Point method, which is a speed-based method, with a traffic flow-based method. The second model integrates two speed-based methods: the Mid-Point method and the Minimum Speed method. In both models, the switch between travel time estimation methods is based on the congestion level and queue status automatically identified by clustering analysis. During incident conditions with rapidly changing queue lengths, shock wave analysis-based refinements are applied for on-line estimation to capture the fast queue propagation and recovery. Travel time estimates obtained from existing speed-based methods, traffic flow-based methods, and the models developed were tested using both simulation and real-world data. The results indicate that all tested methods performed at an acceptable level during periods of low congestion. However, their performances vary with an increase in congestion. Comparisons with other estimation methods also show that the developed hybrid models perform well in all cases. Further comparisons between the on-line and off-line travel time estimation methods reveal that off-line methods perform significantly better only during fast-changing congested conditions, such as during incidents. The impacts of major influential factors on the performance of travel time estimation, including data preprocessing procedures, detector errors, detector spacing, frequency of travel time updates to traveler information devices, travel time link length, and posted travel time range, were investigated in this study. The results show that these factors have more significant impacts on the estimation accuracy and reliability under congested conditions than during uncongested conditions. For the incident conditions, the estimation quality improves with the use of a short rolling period for data smoothing, more accurate detector data, and frequent travel time updates.
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Al, Adaileh Mohammad Ali. "A Travel Time Estimation Model for Facility Location on Real Road Networks." Ohio University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1557421387196019.

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Danielsson, Anna, and Gabriella Gustafsson. "Link flow destination distribution estimation based on observed travel times for traffic prediction during incidents." Thesis, Linköpings universitet, Kommunikations- och transportsystem, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-170080.

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In a lot of big cities, the traffic network is overloaded, with congestion and unnecessary emissions as consequence. Therefore, different traffic control methods are useful, especially in case of an incident. One key problem for traffic control is traffic prediction and the aim of this thesis is to develop, calibrate and evaluate a route flow model using only observed travel times and travel demand as input. The route flow model was used to calculate the metric link flow destination distribution, that presents to which destinations the travelers on a link are going in percentage.
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Books on the topic "Travel time (Traffic estimation)"

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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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Lipps, Oliver. Modellierung der individuellen Verhaltensvariationen bei der Verkehrsentstehung. Karlsruhe, Germany]: Institut für Verkehrswesen, Universität Karlsruhe, 2001.

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Associates, Dowling, Travel Model Improvement Program (U.S.), and Technology Sharing Program (U.S.), eds. Travel model speed estimation and post processing methods for air quality analysis: Final report. [Washington, D.C.]: U.S. Dept. of Transportation, 1997.

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Wilson, James F. Estimating traveltimes of boats through bald eagle habitat along the Snake River, northwestern Wyoming, using geographic information system techniques. Cheyenne, Wyo: U.S. Dept. of the Interior, U.S. Geological Survey, 1992.

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Schiffer, Robert G. Long-Distance and Rural Travel Transferable Parameters for Statewide Travel Forecasting Models. WASHINGTON, D.C: TRANSPORTATION RESEARCH BOARD, 2012.

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Staunton, Michael M. Journey time measurement: For the assessment of major road projects. Dublin: Environmental Research Unit, 1993.

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A, Martin William. Travel estimation techniques for urban planning. Washington, D.C: National Academy Press, 1998.

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Johnson, Dennis L. 20-year traffic forecasting factors. Pierre, SD: South Dakota Dept. of Transportation, Office of Research, 2000.

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National Research Council (U.S.). Transportation Research Board., ed. Capturing the dynamics of travel behavior. Washington, D.C: Transportation Research Board, National Research Council, 1987.

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F, Turnbull Katherine, Texas. Dept. of Transportation., and Texas Transportation Institute, eds. Potential of telecommuting for travel demand management. College Station, Tex: Texas Transportation Institute, Texas A&M University System, 1996.

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Book chapters on the topic "Travel time (Traffic estimation)"

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Treiber, Martin, and Arne Kesting. "Travel Time Estimation." In Traffic Flow Dynamics, 367–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32460-4_19.

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Ban, Xuegang(Jeff), Ryan Herring, J. D. Margulici, and Alexandre M. Bayen. "Optimal Sensor Placement for Freeway Travel Time Estimation." In Transportation and Traffic Theory 2009: Golden Jubilee, 697–721. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-0820-9_34.

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Soriguera Martí, Francesc. "Design of Spot Speed Methods for Real-Time Provision of Traffic Information." In Highway Travel Time Estimation With Data Fusion, 85–107. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-48858-4_4.

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Dembczyński, Krzysztof, Wojciech Kotłowski, Przemysław Gaweł, Adam Szarecki, and Andrzej Jaszkiewicz. "Matrix Factorization for Travel Time Estimation in Large Traffic Networks." In Artificial Intelligence and Soft Computing, 500–510. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38610-7_46.

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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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Zhong, Shaopeng, and Daniel Sun. "Travel Time Estimation Based on Built Environment Attributes and Low-Frequency Floating Car Data." In Logic-Driven Traffic Big Data Analytics, 119–39. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8016-8_6.

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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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Martínez-Díaz, Margarita. "A Simple Algorithm for the Estimation of Road Traffic Space Mean Speeds from Data Available to Most Management Centers." In The Evolution of Travel Time Information Systems, 67–100. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89672-0_3.

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Šilar, Jan, Tomáš Tichý, and Jan Přikryl. "Estimation of Travel Times and Identification of Traffic Excesses on Roads." In Telematics - Support for Transport, 166–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45317-9_18.

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Ciskowski, Piotr, Grzegorz Drzewiński, Marek Bazan, and Tomasz Janiczek. "Estimation of Travel Time in the City Using Neural Networks Trained with Simulated Urban Traffic Data." In Contemporary Complex Systems and Their Dependability, 121–34. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91446-6_13.

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Conference papers on the topic "Travel time (Traffic estimation)"

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Gao, Ruipeng, Xiaoyu Guo, Fuyong Sun, Lin Dai, Jiayan Zhu, Chenxi Hu, and Haibo Li. "Aggressive Driving Saves More Time? Multi-task Learning for Customized Travel Time Estimation." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/234.

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Estimating the origin-destination travel time is a fundamental problem in many location-based services for vehicles, e.g., ride-hailing, vehicle dispatching, and route planning. Recent work has made significant progress to accuracy but they largely rely on GPS traces which are too coarse to model many personalized driving events. In this paper, we propose Customized Travel Time Estimation (CTTE) that fuses GPS traces, smartphone inertial data, and road network within a deep recurrent neural network. It constructs a link traffic database with topology representation, speed statistics, and query distribution. It also uses inertial data to estimate the arbitrary phone's pose in car, and detects fine-grained driving events. The multi-task learning structure predicts both traffic speed at public level and customized travel time at personal level. Extensive experiments on two real-world traffic datasets from Didi Chuxing have demonstrated our effectiveness.
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Zhu, Zhong, and Wei Wang. "A Travel Time Estimation Model for Route Guidance Systems." In Second International Conference on Transportation and Traffic Studies (ICTTS ). Reston, VA: American Society of Civil Engineers, 2000. http://dx.doi.org/10.1061/40503(277)85.

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Shao, Hu, William H. K. Lam, Agachai Sumalee, and Anthony Chen. "Network-Wide Road Travel Time Estimation with Inconsistent Sensor Data." In Seventh International Conference on Traffic and Transportation Studies (ICTTS) 2010. Reston, VA: American Society of Civil Engineers, 2010. http://dx.doi.org/10.1061/41123(383)87.

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Abbar, Sofiane, Rade Stanojevic, and Mohamed Mokbel. "STAD: Spatio-Temporal Adjustment of Traffic-Oblivious Travel-Time Estimation." In 2020 21st IEEE International Conference on Mobile Data Management (MDM). IEEE, 2020. http://dx.doi.org/10.1109/mdm48529.2020.00029.

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Shao, Kangjia, Kun Wang, Lianliang Chen, and Zhengyang Zhou. "Estimation of Urban Travel Time with Sparse Traffic Surveillance Data." In HPCCT & BDAI 2020: 2020 4th High Performance Computing and Cluster Technologies Conference & 2020 3rd International Conference on Big Data and Artificial Intelligence. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3409501.3409539.

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Xu, Tu, and Changlin Wang. "Urban Road Sections Travel Time Estimation Based on Real-time Traffic Information." In 2nd International Conference on Computer Application and System Modeling. Paris, France: Atlantis Press, 2012. http://dx.doi.org/10.2991/iccasm.2012.307.

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He, Shuyan, Wei Guan, Wei Qiu, Lan Wang, and Jihui Ma. "Link Travel Time Estimation at Signalized Road Segments with Floating Car Data." In Sixth International Conference of Traffic and Transportation Studies Congress (ICTTS). Reston, VA: American Society of Civil Engineers, 2008. http://dx.doi.org/10.1061/40995(322)83.

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Zheng, Fangfang, and Henk van Zuylen. "Comparison of Urban Link Travel Time Estimation Models Based on Probe Vehicle Data." In Seventh International Conference on Traffic and Transportation Studies (ICTTS) 2010. Reston, VA: American Society of Civil Engineers, 2010. http://dx.doi.org/10.1061/41123(383)59.

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Li, Jiezhang, Wanyi Zhou, Zebin Chen, and Yue-Jiao Gong. "Geo-Attention Network for Traffic Condition Prediction and Travel Time Estimation." In SIGSPATIAL '21: 29th International Conference on Advances in Geographic Information Systems. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3474717.3488383.

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Xu, Tiandong, Osama Tomeh, and Lijun Sun. "Urban Expressway Real-Time Traffic State Estimation and Travel Time Prediction within EKF Framework." 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)32.

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Reports on the topic "Travel time (Traffic estimation)"

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Lin, Pei-Sung. Coordinated Pre-Preemption of Traffic Signals to Enhance Railroad Grade Crossing Safety in Urban Areas and Estimation of Train Impacts to Arterial Travel Time Delay. Tampa, FL: University of South Florida, January 2004. http://dx.doi.org/10.5038/cutr-nctr-rr-2014-06.

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Day, Christopher, Jason Wasson, Thomas Brennan, and Darcy Bullock. Application of Travel Time Information for Traffic Management. Purdue University, August 2012. http://dx.doi.org/10.5703/1288284314666.

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Chandrayadula, Tarun K. Travel Time Estimation Methods for Mode Tomography. Fort Belvoir, VA: Defense Technical Information Center, September 2010. http://dx.doi.org/10.21236/ada542281.

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Mohammadian, Abolfazl, Homa Taghipour, and Amir Bahador Parsa. Dynamic Travel Time Estimation for Northeast Illinois Expressways. Illinois Center for Transportation, June 2020. http://dx.doi.org/10.36501/0197-9191/20-012.

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Wu, Tong Qiang, Eil Kwon, Kevin Sommers, Michael Zhang, and Ahsan Habib. Arterial Link Travel Time Estimation Using Loop Detector Data. Iowa City, Iowa: University of Iowa Public Policy Center, 1997. http://dx.doi.org/10.17077/zp8m-emq1.

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Mathew, Sonu, and Srinivas S. Pulugurtha. Effect of Weather Events on Travel Time Reliability and Crash Occurrence. Mineta Transportation Institute, November 2022. http://dx.doi.org/10.31979/mti.2022.2035.

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The magnitude of the effect of adverse weather conditions on road operational performance varies with the type of weather condition and the road characteristics of the road links and adjacent links. Therefore, the relationship between weather and traffic is always a concern to traffic engineers and planners, and they have extensively explored ways to integrate weather information into transportation systems. Understanding the influence of weather on operational performance and safety helps traffic engineers and planners to proactively plan and manage transportation systems. The main objective of this research is to evaluate the effect of adverse weather conditions on travel time reliability and crash occurrence, by severity, using weather data, road data, travel time data, and crash data for North Carolina. The methodology and results from this research are useful for transportation system managers and planners to manage the traffic and improve safety under different weather conditions. They also help improve the functionality of weather-responsive management strategies like variable signs to indicate the change in reliability and safety under rainfall and low visibility conditions.
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Duvvuri, Sarvani, and Srinivas S. Pulugurtha. Researching Relationships between Truck Travel Time Performance Measures and On-Network and Off-Network Characteristics. Mineta Transportation Institute, July 2021. http://dx.doi.org/10.31979/mti.2021.1946.

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Trucks serve significant amount of freight tonnage and are more susceptible to complex interactions with other vehicles in a traffic stream. While traffic congestion continues to be a significant ‘highway’ problem, delays in truck travel result in loss of revenue to the trucking companies. There is a significant research on the traffic congestion mitigation, but a very few studies focused on data exclusive to trucks. This research is aimed at a regional-level analysis of truck travel time data to identify roads for improving mobility and reducing congestion for truck traffic. The objectives of the research are to compute and evaluate the truck travel time performance measures (by time of the day and day of the week) and use selected truck travel time performance measures to examine their correlation with on-network and off-network characteristics. Truck travel time data for the year 2019 were obtained and processed at the link level for Mecklenburg County, Wake County, and Buncombe County, NC. Various truck travel time performance measures were computed by time of the day and day of the week. Pearson correlation coefficient analysis was performed to select the average travel time (ATT), planning time index (PTI), travel time index (TTI), and buffer time index (BTI) for further analysis. On-network characteristics such as the speed limit, reference speed, annual average daily traffic (AADT), and the number of through lanes were extracted for each link. Similarly, off-network characteristics such as land use and demographic data in the near vicinity of each selected link were captured using 0.25 miles and 0.50 miles as buffer widths. The relationships between the selected truck travel time performance measures and on-network and off-network characteristics were then analyzed using Pearson correlation coefficient analysis. The results indicate that urban areas, high-volume roads, and principal arterial roads are positively correlated with the truck travel time performance measures. Further, the presence of agricultural, light commercial, heavy commercial, light industrial, single-family residential, multi-family residential, office, transportation, and medical land uses increase the truck travel time performance measures (decrease the operational performance). The methodological approach and findings can be used in identifying potential areas to serve as truck priority zones and for planning decentralized delivery locations.
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Arhin, Stephen, Babin Manandhar, Kevin Obike, and Melissa Anderson. Impact of Dedicated Bus Lanes on Intersection Operations and Travel Time Model Development. Mineta Transportation Institute, June 2022. http://dx.doi.org/10.31979/mti.2022.2040.

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Over the years, public transit agencies have been trying to improve their operations by continuously evaluating best practices to better serve patrons. Washington Metropolitan Area Transit Authority (WMATA) oversees the transit bus operations in the Washington Metropolitan Area (District of Columbia, some parts of Maryland and Virginia). One practice attempted by WMATA to improve bus travel time and transit reliability has been the implementation of designated bus lanes (DBLs). The District Department of Transportation (DDOT) implemented a bus priority program on selected corridors in the District of Columbia leading to the installation of red-painted DBLs on corridors of H Street, NW, and I Street, NW. This study evaluates the impacts on the performance of transit buses along with the general traffic performance at intersections on corridors with DBLs installed in Washington, DC by using a “before” and “after” approach. The team utilized non-intrusive video data to perform vehicular turning movement counts to assess the traffic flow and delays (measures of effectiveness) with a traffic simulation software. Furthermore, the team analyzed the Automatic Vehicle Locator (AVL) data provided by WMATA for buses operating on the study segments to evaluate bus travel time. The statistical analysis showed that the vehicles traveling on H Street and I Street (NW) experienced significantly lower delays during both AM (7:00–9:30 AM) and PM (4:00–6:30 PM) peak hours after the installation of bus lanes. The approximation error metrics (normalized squared errors) for the testing dataset was 0.97, indicating that the model was predicting bus travel times based on unknown data with great accuracy. WMATA can apply this research to other segments with busy bus schedules and multiple routes to evaluate the need for DBLs. Neural network models can also be used to approximate bus travel times on segments by simulating scenarios with DBLs to obtain accurate bus travel times. Such implementation could not only improve WMATA’s bus service and reliability but also alleviate general traffic delays.
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Freshley, M. D., and M. J. Graham. Estimation of ground-water travel time at the Hanford Site: Description, past work, and future needs. Office of Scientific and Technical Information (OSTI), January 1988. http://dx.doi.org/10.2172/7045828.

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Kodupuganti, Swapneel R., Sonu Mathew, and Srinivas S. Pulugurtha. Modeling Operational Performance of Urban Roads with Heterogeneous Traffic Conditions. Mineta Transportation Institute, January 2021. http://dx.doi.org/10.31979/mti.2021.1802.

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The rapid growth in population and related demand for travel during the past few decades has had a catalytic effect on traffic congestion, air quality, and safety in many urban areas. Transportation managers and planners have planned for new facilities to cater to the needs of users of alternative modes of transportation (e.g., public transportation, walking, and bicycling) over the next decade. However, there are no widely accepted methods, nor there is enough evidence to justify whether such plans are instrumental in improving mobility of the transportation system. Therefore, this project researches the operational performance of urban roads with heterogeneous traffic conditions to improve the mobility and reliability of people and goods. A 4-mile stretch of the Blue Line light rail transit (LRT) extension, which connects Old Concord Rd and the University of North Carolina at Charlotte’s main campus on N Tryon St in Charlotte, North Carolina, was considered for travel time reliability analysis. The influence of crosswalks, sidewalks, trails, greenways, on-street bicycle lanes, bus/LRT routes and stops/stations, and street network characteristics on travel time reliability were comprehensively considered from a multimodal perspective. Likewise, a 2.5-mile-long section of the Blue Line LRT extension, which connects University City Blvd and Mallard Creek Church Rd on N Tryon St in Charlotte, North Carolina, was considered for simulation-based operational analysis. Vissim traffic simulation software was used to compute and compare delay, queue length, and maximum queue length at nine intersections to evaluate the influence of vehicles, LRT, pedestrians, and bicyclists, individually and/or combined. The statistical significance of variations in travel time reliability were particularly less in the case of links on N Tryon St with the Blue Line LRT extension. However, a decrease in travel time reliability on some links was observed on the parallel route (I-85) and cross-streets. While a decrease in vehicle delay on northbound and southbound approaches of N Tryon St was observed in most cases after the LRT is in operation, the cross-streets of N Tryon St incurred a relatively higher increase in delay after the LRT is in operation. The current pedestrian and bicycling activity levels seemed insignificant to have an influence on vehicle delay at intersections. The methodological approaches from this research can be used to assess the performance of a transportation facility and identify remedial solutions from a multimodal perspective.
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