Увійти

Готові списки джерел за темами / Models of time travel / Статті в журналах

Статті в журналах з теми "Models of time travel"

Щоб переглянути інші типи публікацій з цієї теми, перейдіть за посиланням: Models of time travel.

Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 28 січня 2023

Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями

Оберіть тип джерела:

Ознайомтеся з топ-50 статей у журналах для дослідження на тему "Models of time travel".

Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.

Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.

Переглядайте статті в журналах для різних дисциплін та оформлюйте правильно вашу бібліографію.

1

Roden, David B. "Forecasting Travel Time." Transportation Research Record: Journal of the Transportation Research Board 1518, no. 1 (January 1996): 7–12. http://dx.doi.org/10.1177/0361198196151800102.

Повний текст джерела

Анотація:

If travel time and speed are to be used as critical performance measures in congestion management systems and air quality analysis procedures, existing modeling techniques will need to be enhanced. Many of the simplifying assumptions that are built into traditional modeling techniques are described. Several relatively simple enhancements to existing models that can greatly improve the model's ability to estimate travel time and speeds are identified, and more advanced methods that could be considered as part of major model redevelopment efforts or detailed air quality studies are suggested. One of these methods involves simulation techniques. The problems and issues of integrating simulation models with travel demand forecasting techniques are outlined, and it is concluded that modeling speed is considerably more difficult than modeling volumes. The bottom-line criterion for any model enhancement is that the procedure supports decision makers in a timely and cost-effective way. This criterion is likely to limit the types of enhancements that are possible.

Стилі APA, Harvard, Vancouver, ISO та ін.

2

Daly, Hannah E., Kalai Ramea, Alessandro Chiodi, Sonia Yeh, Maurizio Gargiulo, and Brian Ó. Gallachóir. "Incorporating travel behaviour and travel time into TIMES energy system models." Applied Energy 135 (December 2014): 429–39. http://dx.doi.org/10.1016/j.apenergy.2014.08.051.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

3

Carey, Malachy, and Y. E. Ge. "Comparing whole-link travel time models." Transportation Research Part B: Methodological 37, no. 10 (December 2003): 905–26. http://dx.doi.org/10.1016/s0191-2615(02)00091-7.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

4

Yang, Shu, and Yao-Jan Wu. "Mixture Models for Fitting Freeway Travel Time Distributions and Measuring Travel Time Reliability." Transportation Research Record: Journal of the Transportation Research Board 2594, no. 1 (January 2016): 95–106. http://dx.doi.org/10.3141/2594-13.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

5

Carey, Malachy, Paul Humphreys, Marie McHugh, and Ronan McIvor. "Travel-Time Models With and Without Homogeneity Over Time." Transportation Science 51, no. 3 (August 2017): 882–92. http://dx.doi.org/10.1287/trsc.2016.0674.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

6

van Hinsbergen, C. P. IJ, and J. W. C. van Lint. "Bayesian Combination of Travel Time Prediction Models." Transportation Research Record: Journal of the Transportation Research Board 2064, no. 1 (January 2008): 73–80. http://dx.doi.org/10.3141/2064-10.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

7

Carey, Malachy, and Y. E. Ge. "Efficient Discretisation for Link Travel Time Models." Networks and Spatial Economics 4, no. 3 (September 2004): 269–90. http://dx.doi.org/10.1023/b:nets.0000039783.57975.f0.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

8

MacGregor Smith, J., and F. R. B. Cruz. "state dependent travel time models and properties." Physica A: Statistical Mechanics and its Applications 395 (February 2014): 560–79. http://dx.doi.org/10.1016/j.physa.2013.10.048.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

9

Mamdoohi, Amir Reza, Amin Delfan Azari, and Mehrdad Alomoradi. "Estimating Bus Travel Time Using Survival Models." Journal of Planning and Budgeting 24, no. 3 (December 1, 2019): 111–32. http://dx.doi.org/10.29252/jpbud.24.3.111.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

10

Kuehnel, Nico, Dominik Ziemke, Rolf Moeckel, and Kai Nagel. "The end of travel time matrices: Individual travel times in integrated land use/transport models." Journal of Transport Geography 88 (October 2020): 102862. http://dx.doi.org/10.1016/j.jtrangeo.2020.102862.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

11

Gupta, Surabhi, Peter Vovsha, Arup Dutta, Vladimir Livshits, Wang Zhang, and Haidong Zhu. "Incorporation of Travel Time Reliability in Regional Travel Model." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 49 (September 11, 2018): 46–57. http://dx.doi.org/10.1177/0361198118787090.

Повний текст джерела

Анотація:

The paper presents a practical method for incorporation of travel time reliability in a regional travel model. The discussion includes five consecutive steps. First it describes how the vehicle speed dataset for the metropolitan area of Phoenix, AZ, termed HERE, was processed and link-level volume–delay–reliability functions were estimated, and then how link-level reliability measures can be applied for network path building. The third step describes how trip origin–destination (OD) reliability measures can be constructed out of the link-level reliability measures. The fourth step involves implementation of the link-level and OD-level reliability measures in highway assignment, mode choice, and other travel models. The fifth step includes model validation and sensitivity tests. The paper addresses several long-standing issues associated with incorporation of travel time reliability in operational travel models in practice. These issues include construction of OD reliability measures with the recognition that the core link-level reliability measures such as standard deviation or variance are not additive in a general case, accounting for a partial correlation between travel time distributions for different links, and incorporation of travel time reliability in a standard static assignment.

Стилі APA, Harvard, Vancouver, ISO та ін.

12

Miller-Hooks, Elise, and Baiyu Yang. "Impact of Travel Time Models on Quality of Real-Time Routing Instructions." Transportation Research Record: Journal of the Transportation Research Board 1857, no. 1 (January 2003): 21–29. http://dx.doi.org/10.3141/1857-03.

Повний текст джерела

Анотація:

Mobile communication systems coupled with intelligent transportation systems technologies can permit information service providers to supply real-time routing instructions to suitably equipped vehicles as real-time travel times are received. Simply considering current conditions in updating routing decisions, however, may lead to suboptimal path choices, because future travel conditions likely will differ from that currently observed. Even with perfect and continuously updated information about current conditions, future travel times can be known a priori with uncertainty at best. Further, in congested transportation systems, conditions vary over time as recurrent congestion may change with a foreseeable pattern during peak driving hours. It is postulated that better, more robust routing instructions can be provided by explicitly accounting for this inherent stochastic and dynamic nature of future travel conditions in generating the routing instructions. It is further hypothesized that nearly equally good routing instructions can be provided by collecting real-time information from only a small neighborhood within the transportation system as from the entire system. Extensive numerical experiments were conducted to assess the validity of these two hypotheses.

Стилі APA, Harvard, Vancouver, ISO та ін.

13

Li, Ruimin, Geoffrey Rose, and Majid Sarvi. "Evaluation of Speed-Based Travel Time Estimation Models." Journal of Transportation Engineering 132, no. 7 (July 2006): 540–47. http://dx.doi.org/10.1061/(asce)0733-947x(2006)132:7(540).

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

14

Moradi, H., S. M. Hanasoge, and P. S. Cally. "NUMERICAL MODELS OF TRAVEL-TIME INHOMOGENEITIES IN SUNSPOTS." Astrophysical Journal 690, no. 1 (December 8, 2008): L72—L75. http://dx.doi.org/10.1088/0004-637x/690/1/l72.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

15

Moylan, Emily K. M., and Taha Hossein Rashidi. "Latent-Segmentation, Hazard-Based Models of Travel Time." IEEE Transactions on Intelligent Transportation Systems 18, no. 8 (August 2017): 2174–80. http://dx.doi.org/10.1109/tits.2016.2636321.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

16

Miller, Kristie. "Time travel and the open future." Disputatio 1, no. 19 (November 1, 2005): 223–32. http://dx.doi.org/10.2478/disp-2005-0009.

Повний текст джерела

Анотація:

Abstract I argue that the thesis that time travel is logically possible, is inconsistent with the necessary truth of any of the usual ‘open future-objective present’ models of the universe. It has been relatively uncontroversial until recently to hold that presentism is inconsistent with the possibility of time travel. I argue that recent arguments to the contrary do not show that presentism is consistent with time travel. Moreover, the necessary truth of other open future-objective present models which we might, prima facie, have supposed to be more amenable to the possibility of time travel, turn out also to be inconsistent with this possibility.

Стилі APA, Harvard, Vancouver, ISO та ін.

17

Kwon, Jaimyoung, Benjamin Coifman, and Peter Bickel. "Day-to-Day Travel-Time Trends and Travel-Time Prediction from Loop-Detector Data." Transportation Research Record: Journal of the Transportation Research Board 1717, no. 1 (January 2000): 120–29. http://dx.doi.org/10.3141/1717-15.

Повний текст джерела

Анотація:

An approach is presented for estimating future travel times on a freeway using flow and occupancy data from single-loop detectors and historical travel-time information. Linear regression, with the stepwise-variable-selection method and more advanced tree-based methods, is used. The analysis considers forecasts ranging from a few minutes into the future up to an hour ahead. Leave-a-day-out cross-validation was used to evaluate the prediction errors without underestimation. The current traffic state proved to be a good predictor for the near future, up to 20 min, whereas historical data are more informative for longer-range predictions. Tree-based methods and linear regression both performed satisfactorily, showing slightly different qualitative behaviors for each condition examined in this analysis. Unlike preceding works that rely on simulation, real traffic data were used. Although the current implementation uses measured travel times from probe vehicles, the ultimate goal is an autonomous system that relies strictly on detector data. In the course of presenting the prediction system, the manner in which travel times change from day to day was examined, and several metrics to quantify these changes were developed. The metrics can be used as input for travel-time prediction, but they also should be beneficial for other applications, such as calibrating traffic models and planning models.

Стилі APA, Harvard, Vancouver, ISO та ін.

18

Shen, Yindong, Jia Xu, Xianyi Wu, and Yudong Ni. "MODELLING TRAVEL TIME DISTRIBUTION AND ITS INFLUENCE OVER STOCHASTIC VEHICLE SCHEDULING." Transport 34, no. 3 (March 21, 2019): 237–49. http://dx.doi.org/10.3846/transport.2019.8940.

Повний текст джерела

Анотація:

Due to the paucity of well-established modelling approaches or well-accepted travel time distributions, the existing travel time models are often assumed to follow certain popular distributions, such as normal or lognormal, which may lead to results deviating from actual ones. This paper proposes a modelling approach for travel times using distribution fitting methods based on the data collected by Automatic Vehicle Location (AVL) systems. By this proposed approach, a compound travel time model can be built, which consists of the best distribution models for the travel times in each period of a day. Applying to stochastic vehicle scheduling, the influence of different travel time models is further studied. Results show that the compound model can fit more precisely to the actual travel times under various traffic situations, whilst the on-time performance of resulting vehicle schedules can be improved. The research findings have also potential benefit for the other research based on travel time models in public transport including timetabling, service planning and reliability measurement.

Стилі APA, Harvard, Vancouver, ISO та ін.

19

Guo, Feng, Qing Li, and Hesham Rakha. "Multistate Travel Time Reliability Models with Skewed Component Distributions." Transportation Research Record: Journal of the Transportation Research Board 2315, no. 1 (January 2012): 47–53. http://dx.doi.org/10.3141/2315-05.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

20

Abdalla, M. Fathy, and Mohamed Abdel-Aty. "Modeling Travel Time Under ATIS Using Mixed Linear Models." Transportation 33, no. 1 (January 2006): 63–82. http://dx.doi.org/10.1007/s11116-005-5354-y.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

21

Long, Jiancheng, Hai-Jun Huang, and Ziyou Gao. "Discretised route travel time models based on cumulative flows." Journal of Advanced Transportation 47, no. 1 (May 28, 2012): 105–25. http://dx.doi.org/10.1002/atr.1192.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

22

Bai, Cong, Zhong-Ren Peng, Qing-Chang Lu, and Jian Sun. "Dynamic Bus Travel Time Prediction Models on Road with Multiple Bus Routes." Computational Intelligence and Neuroscience 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/432389.

Повний текст джерела

Анотація:

Accurate and real-time travel time information for buses can help passengers better plan their trips and minimize waiting times. A dynamic travel time prediction model for buses addressing the cases on road with multiple bus routes is proposed in this paper, based on support vector machines (SVMs) and Kalman filtering-based algorithm. In the proposed model, the well-trained SVM model predicts the baseline bus travel times from the historical bus trip data; the Kalman filtering-based dynamic algorithm can adjust bus travel times with the latest bus operation information and the estimated baseline travel times. The performance of the proposed dynamic model is validated with the real-world data on road with multiple bus routes in Shenzhen, China. The results show that the proposed dynamic model is feasible and applicable for bus travel time prediction and has the best prediction performance among all the five models proposed in the study in terms of prediction accuracy on road with multiple bus routes.

Стилі APA, Harvard, Vancouver, ISO та ін.

23

Jeong, Ranhee, and Laurence R. Rilett. "Prediction Model of Bus Arrival Time for Real-Time Applications." Transportation Research Record: Journal of the Transportation Research Board 1927, no. 1 (January 2005): 195–204. http://dx.doi.org/10.1177/0361198105192700123.

Повний текст джерела

Анотація:

Advanced traveler information systems (ATIS) are one component of intelligent transportation systems (ITS), and a major component of ATIS is travel time information. Automatic vehicle location (AVL) systems, which are a part of ITS, have been adopted by many transit agencies to track their vehicles and to predict travel time in real time. Because of the complexity involved, there is no universally adopted approach for this latter application, and research is needed in this area. The objectives of the research in this paper are to develop a model to predict bus arrival time using AVL data and apply the model for real-time applications. The test bed was a bus route located in Houston, Texas, and the travel time prediction model considered schedule adherence, traffic congestion, and dwell times. A historical data-based model, regression models, and artificial neural network (ANN) models were used to predict bus arrival time. It was found that ANN models outperformed both the historical data-based model and the regression model in terms of prediction accuracy. It was also found that the ANN models can be used for real-time applications.

Стилі APA, Harvard, Vancouver, ISO та ін.

24

Börjesson, Maria. "Inter-temporal variation in the travel time and travel cost parameters of transport models." Transportation 41, no. 2 (July 30, 2013): 377–96. http://dx.doi.org/10.1007/s11116-013-9493-2.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

25

Mahmudah, Amirotul M. H., A. Budiarto, and S. J. Legowo. "Travel Time Estimation Based on Spot Speed with Instantaneous and Time Slice Model." Applied Mechanics and Materials 776 (July 2015): 80–86. http://dx.doi.org/10.4028/www.scientific.net/amm.776.80.

Повний текст джерела

Анотація:

In off-line applications, travel time is the main parameter of road performance which can be the main consideration for evaluation and planning of transportation policy, and also to assess the accuracy of transportation modeling. While in on-line application travel time is main information for road users to define their travel behavior. Due to the important of travel time, therefore accurate estimation/prediction of travel time is essential. In order to fulfill it, this research analyzed the accuracy of Instantaneous and Time Slice model, and also evaluate the validity of Time mean speed and Space mean speed in mixed traffic condition. There is not much difference in travel time estimation error between models. The travel time estimation was larger than the actual travel time by floating car. It was also found that the error occurred on time mean speed are less than the space mean speed.

Стилі APA, Harvard, Vancouver, ISO та ін.

26

Moeckel, Rolf, Nico Kuehnel, Carlos Llorca, Ana Tsui Moreno, and Hema Rayaprolu. "Agent-Based Simulation to Improve Policy Sensitivity of Trip-Based Models." Journal of Advanced Transportation 2020 (February 25, 2020): 1–13. http://dx.doi.org/10.1155/2020/1902162.

Повний текст джерела

Анотація:

The most common travel demand model type is the trip-based model, despite major shortcomings due to its aggregate nature. Activity-based models overcome many of the limitations of the trip-based model, but implementing and calibrating an activity-based model is labor-intensive and running an activity-based model often takes long runtimes. This paper proposes a hybrid called MITO (Microsimulation Transport Orchestrator) that overcomes some of the limitations of trip-based models, yet is easier to implement than an activity-based model. MITO uses microsimulation to simulate each household and person individually. After trip generation, the travel time budget in minutes is calculated for every household. This budget influences destination choice; i.e., people who spent a lot of time commuting are less likely to do much other travel, while people who telecommute might compensate by additional discretionary travel. Mode choice uses a nested logit model, and time-of-day choice schedules trips in 1-minute intervals. Three case studies demonstrate how individuals may be traced through the entire model system from trip generation to the assignment.

Стилі APA, Harvard, Vancouver, ISO та ін.

27

Azevedo, Chris. "Alternatives For Incorporating Opportunity Cost Of Time In Recreation Demand Models." Journal of Business & Economics Research (JBER) 9, no. 12 (November 22, 2011): 1. http://dx.doi.org/10.19030/jber.v9i12.6601.

Повний текст джерела

Анотація:

The importance of accounting for a respondents travel time in recreation demand models is well established. In practice, most analysts use a fixed fraction of the respondents wage rate to value travel time. However, other approaches have been suggested in the literature. In this paper revealed and stated preference data on Iowa wetland usage is used to explore various specifications of travel time. It is shown that the choice of a particular specification has a direct impact on welfare estimates as well as the consistency between revealed and stated preference data.

Стилі APA, Harvard, Vancouver, ISO та ін.

28

Comi, Antonio, Mykola Zhuk, Volodymyr Kovalyshyn, and Volodymyr Hilevych. "Investigating bus travel time and predictive models: a time series-based approach." Transportation Research Procedia 45 (2020): 692–99. http://dx.doi.org/10.1016/j.trpro.2020.02.109.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

29

Kidando, Emmanuel, Ren Moses, Eren E. Ozguven, and Thobias Sando. "Bayesian Nonparametric Model for Estimating Multistate Travel Time Distribution." Journal of Advanced Transportation 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/5069824.

Повний текст джерела

Анотація:

Multistate models, that is, models with more than two distributions, are preferred over single-state probability models in modeling the distribution of travel time. Literature review indicated that the finite multistate modeling of travel time using lognormal distribution is superior to other probability functions. In this study, we extend the finite multistate lognormal model of estimating the travel time distribution to unbounded lognormal distribution. In particular, a nonparametric Dirichlet Process Mixture Model (DPMM) with stick-breaking process representation was used. The strength of the DPMM is that it can choose the number of components dynamically as part of the algorithm during parameter estimation. To reduce computational complexity, the modeling process was limited to a maximum of six components. Then, the Markov Chain Monte Carlo (MCMC) sampling technique was employed to estimate the parameters’ posterior distribution. Speed data from nine links of a freeway corridor, aggregated on a 5-minute basis, were used to calculate the corridor travel time. The results demonstrated that this model offers significant flexibility in modeling to account for complex mixture distributions of the travel time without specifying the number of components. The DPMM modeling further revealed that freeway travel time is characterized by multistate or single-state models depending on the inclusion of onset and offset of congestion periods.

Стилі APA, Harvard, Vancouver, ISO та ін.

30

Hou, Yi, and Praveen Edara. "Network Scale Travel Time Prediction using Deep Learning." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 45 (June 11, 2018): 115–23. http://dx.doi.org/10.1177/0361198118776139.

Повний текст джерела

Анотація:

In recent years, deep learning models have been receiving increased attention within the artificial intelligence (AI) community because of their high prediction accuracy. In this paper, two deep learning models, long short-term memory (LSTM) and convolutional neural network (CNN), are proposed to predict travel time in a road network. One major advantage of using deep learning for travel time prediction is that it can make accurate predictions for all the segments in the transportation network with a single model structure, instead of building customized models for each segment separately. The proposed models were evaluated on a transportation network in the City of Saint Louis, Missouri. The prediction results show that deep learning can provide accurate prediction for both congested and uncongested traffic conditions, and can successfully capture the traffic dynamics of unexpected incidents or special events. The study findings show that deep learning offers a promising approach to real-time prediction of travel times on a network scale.

Стилі APA, Harvard, Vancouver, ISO та ін.

31

Li, Shu-Guang. "DETERMINATION OF OPTIMAL WORK START TIME." TRANSPORT 22, no. 1 (March 31, 2007): 45–49. http://dx.doi.org/10.3846/16484142.2007.9638095.

Повний текст джерела

Анотація:

The question is: whether the system total travel cost and travel time are reduced by adjusting the work start time or not? This paper proposes the two‐level model for answering the question; the upper‐level minimizes the system travel cost and travel time by using the work start time as a decision variable, the lower‐level models the stochastic dynamic simultaneous route/departure time equilibrium problem. Finally, numerical results of a small network are provided to illustrate the behavior of the model.

Стилі APA, Harvard, Vancouver, ISO та ін.

32

Juhász, Mattias, Tamás Mátrai, and Csaba Koren. "Forecasting travel time reliability in urban road transpo." Archives of Transport 43, no. 3 (September 13, 2017): 53–67. http://dx.doi.org/10.5604/01.3001.0010.4227.

Повний текст джерела

Анотація:

Assessment of travel time reliability as a fundamental factor in travel behaviour has become a very important aspect in both transport modelling and economic appraisal. Improved reliability could provide a significant economic benefit if it is adequately calculated in cost-benefit analyses for which the theoretical background has already been set. However, methods to forecast travel time reliability as well as travel behaviour models including its effects are rather scarce and there is a need for development in this field. Another important aspect could be the influencing factor of reliability in travel demand management and related policy-making. Therefore, this paper intends to further analyse reliability focusing exclusively on urban road transport based on automatic measurements of journey times and traffic volumes from a dataset of the city of Budapest. The main finding and the novelty of the study is a model which can forecast the standard deviation of travel times based on the volume-capacity ratio and the free-flow travel time. The paper also provides a real-life numerical experiment in which the proposed model has been compared with other, existing ones. It proves that besides existing mean-delay-based models, travel time reliability can be forecasted based on the volume-capacity ratio with an adequate accuracy.

Стилі APA, Harvard, Vancouver, ISO та ін.

33

Shi, Li Juan, Ting Jing, Xiao Hong Chen, and Dong Xiu Ou. "The Effects of Rainfalls on Expressway Travel Time." Applied Mechanics and Materials 361-363 (August 2013): 2255–61. http://dx.doi.org/10.4028/www.scientific.net/amm.361-363.2255.

Повний текст джерела

Анотація:

This paper presents an investigation of the effects of rainfall with different levels of precipitation intensity on urban expressway travel time under free-flow speed conditions. The traffic data and corresponding weather data from the expressway section of Longyang in Shanghai for more than one year were used. Statistical analysis was applied to investigate the effects of rainfall on travel time quantitatively. There are two major contributions of this paper. Firstly, four levels of rainfall have varying degrees of significant impacts on travel time in terms of average travel time. Slight rain has no influence on variability of travel time, while heavier rain increases the variability. Secondly, three travel time stochastic models: Normal, Lognormal and Weibull, were proposed. The Lognormal model is the best-fit model under good weather, slight and moderate rainfall conditions, while both Weibull model and Lognormal model are the preferable models under heavy rain and rainstorm conditions. The recommended Lognormal model can be further used for evaluating the performance of the Longyang expressway section in terms of travel time reliability under good weather, slight rain, moderate rain, heavy rain and rainstorm conditions respectively.

Стилі APA, Harvard, Vancouver, ISO та ін.

34

Lloyd-Smith, Patrick, Joshua K. Abbott, Wiktor Adamowicz, and Daniel Willard. "Intertemporal Substitution in Travel Cost Models with Seasonal Time Constraints." Land Economics 96, no. 3 (June 24, 2020): 399–417. http://dx.doi.org/10.3368/le.96.3.399.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

35

Yang, Xia, Rui Ma, Peng Yang, and Xuegang Jeff Ban. "Link Travel Time Estimation in Double-Queue-Based Traffic Models." Promet - Traffic&Transportation 33, no. 3 (May 31, 2021): 387–97. http://dx.doi.org/10.7307/ptt.v33i3.3515.

Повний текст джерела

Анотація:

Double queue concept has gained its popularity in dynamic user equilibrium (DUE) modeling because it can properly model real traffic dynamics. While directly solving such double-queue-based DUE problems is extremely challenging, an approximation scheme called first-order approximation was proposed to simplify the link travel time estimation of DUE problems in a recent study without evaluating its properties and performance. This paper focuses on directly investigating the First-In-First-Out property and the performance of the first-order approximation in link travel time estimation by designing and modeling dynamic network loading (DNL) on single-line stretch networks. After model formulation, we analyze the First-In-First-Out (FIFO) property of the first-order approximation. Then a series of numerical experiments is conducted to demonstrate the FIFO property of the first-order approximation, and to compare its performance with those using the second-order approximation, a point queue model, and the cumulative inflow and exit flow curves. The numerical results show that the first-order approximation does not guarantee FIFO and also suggest that the second-order approximation is recommended especially when the link exit flow is increasing. The study provides guidance for further study on proposing new methods to better estimate link travel times.

Стилі APA, Harvard, Vancouver, ISO та ін.

36

Wang, Kun, Yiming Yang, and Ruixue Li. "Travel time models for the rack-moving mobile robot system." International Journal of Production Research 58, no. 14 (August 16, 2019): 4367–85. http://dx.doi.org/10.1080/00207543.2019.1652778.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

37

SAKAI, Katsuya, Takahiko KUSAKABE, Chong WEI, and Yasuo ASAKURA. "STATISTICAL ANALYSIS ON PERFORMANCE OF TRAVEL TIME INTERVAL PREDICTION MODELS." Journal of Japan Society of Civil Engineers, Ser. D3 (Infrastructure Planning and Management) 68, no. 5 (2012): I_1297—I_1307. http://dx.doi.org/10.2208/jscejipm.68.i_1297.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

38

Barkley, Tiffany, Rob Hranac, and Karl Petty. "Relating Travel Time Reliability and Nonrecurrent Congestion with Multistate Models." Transportation Research Record: Journal of the Transportation Research Board 2278, no. 1 (January 2012): 13–20. http://dx.doi.org/10.3141/2278-02.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

39

Gentili, M., and Pitu B. Mirchandani. "Review of optimal sensor location models for travel time estimation." Transportation Research Part C: Emerging Technologies 90 (May 2018): 74–96. http://dx.doi.org/10.1016/j.trc.2018.01.021.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

40

Nutakor, Chris, and Frank Navin. "Urban travel time models: a case study of Vancouver (BC)." Transportation Planning and Technology 18, no. 4 (October 1994): 263–82. http://dx.doi.org/10.1080/03081069408717549.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

41

Behounkova, M., H. Cizkova, and C. Matyska. "Resolution tests of global geodynamic models by travel-time tomography." Studia Geophysica et Geodaetica 49, no. 3 (July 2005): 343–63. http://dx.doi.org/10.1007/s11200-005-0014-4.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

42

Dietrich, C. R., A. J. Jakeman, and G. A. Thomas. "Statistical models for solute travel time under unsteady flow conditions." Journal of Hydrology 88, no. 3-4 (November 1986): 253–74. http://dx.doi.org/10.1016/0022-1694(86)90094-6.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

43

Kajalić, Jelena, Nikola Čelar, and Stamenka Stanković. "Travel Time Estimation on Urban Street Segment." PROMET - Traffic&Transportation 30, no. 1 (February 23, 2018): 115–20. http://dx.doi.org/10.7307/ptt.v30i1.2473.

Повний текст джерела

Анотація:

Level of service (LOS) is used as the main indicator of transport quality on urban roads and it is estimated based on the travel speed. The main objective of this study is to determine which of the existing models for travel speed calculation is most suitable for local conditions. The study uses actual data gathered in travel time survey on urban streets, recorded by applying second by second GPS data. The survey is limited to traffic flow in saturated conditions. The RMSE method (Root Mean Square Error) is used for research results comparison with relevant models: Akcelik, HCM (Highway Capacity Manual), Singapore model and modified BPR (the Bureau of Public Roads) function (Dowling - Skabardonis). The lowest deviation in local conditions for urban streets with standardized intersection distance (400-500 m) is demonstrated by Akcelik model. However, for streets with lower signal density (<1 signal/km) the correlation between speed and degree of saturation is best presented by HCM and Singapore model. According to test results, Akcelik model was adopted for travel speed estimation which can be the basis for determining the level of service in urban streets with standardized intersection distance and coordinated signal timing under local conditions.

Стилі APA, Harvard, Vancouver, ISO та ін.

44

Park, Dongjoo, Laurence R. Rilett, Parichart Pattanamekar, and Keechoo Choi. "Estimating Travel Time Summary Statistics of Larger Intervals from Smaller Intervals Without Storing Individual Data." Transportation Research Record: Journal of the Transportation Research Board 1804, no. 1 (January 2002): 39–47. http://dx.doi.org/10.3141/1804-06.

Повний текст джерела

Анотація:

Historically, real-time intelligent transportation systems data are aggregated into discrete periods, typically of 5 to 10 min duration, and are subsequently used for travel time estimation and forecasting. In a previous study of link and corridor travel time estimation and forecasting by using probe vehicles, it was shown that the optimal aggregation interval size is a function of the traffic condition and the application. It is expected that traffic management centers will continue to collect travel time statistics (e.g., mean and variance) from probe vehicles and archive this data at a minimum time interval. Statistical models are developed for estimating the mean and variance of the link and route or corridor travel time for a larger interval by using only the observed mean travel time and variance for each smaller or basic interval. The proposed models are demonstrated by using travel time data obtained from Houston, Texas, which were collected as part of the automatic vehicle identification system of the Houston TranStar system. It was found that the proposed models for estimating link travel time mean and variance for a larger interval were easy to implement and provided results that had minimal error. The route or corridor travel time mean and variance model had considerable error compared with the link travel time mean and variance models.

Стилі APA, Harvard, Vancouver, ISO та ін.

45

Guo, Xiao, and Huijun Sun. "Analysis of Time of Day Fare Discounts on Urban Mass Transit Travel Behavior, Crowding, and Waiting Time." Mathematical Problems in Engineering 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/686705.

Повний текст джерела

Анотація:

Every morning, commuters select the regularly dispatched urban mass transit for traveling from a residential area to a workplace. This paper aims to find an optimal discount fare and time intervals on morning peak hour. As a direct and flexible traffic economic instrument, fares can influence commuters’ behavior. Therefore, fare discount has been proposed to regulate traffic flow in different time. Two models have been analyzed to describe it with schedule delay because of the travel demand size. The first objective function is constructed on pressure equalization when the travel demand is small. The other objective function is to minimize total waiting time when the travel demand is large. In the end, numerical examples based on an artificial network are performed to characterize fare discount models.

Стилі APA, Harvard, Vancouver, ISO та ін.

46

Roy, Serin Sara, and Anil R. "Estimation of Travel Time in Urban Streets Using Various Modes." International Journal of Innovative Research in Engineering & Management 3, no. 6 (November 17, 2016): 531–35. http://dx.doi.org/10.21276/ijirem.2016.3.6.14.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

47

Ding, Ling, and Xu Yang. "The Response of Urban Travel Mode Choice to Parking Fees considering Travel Time Variability." Advances in Civil Engineering 2020 (July 29, 2020): 1–9. http://dx.doi.org/10.1155/2020/8969202.

Повний текст джерела

Анотація:

Increasing automobile use leads to higher costs for traveling associated with emissions, congestion, noise, and other impacts. One option to address this is to introduce high parking charges to reduce the demand for automobile use and encourage the travel mode switch to public transport. To estimate commuters’ mode choice behavior in response to high parking fees, commuters from Nanjing completed an individually customized discrete choice survey in which they chose between driving and taking the bus or metro when choices varied in terms of time and cost attributes. Multinomial logit models were used to estimate commuters’ responses to high parking charges. In the models, the variability of travel times is considered and analyzed in the stated mode choice models. The results suggest that increases in costs of driving will lead to a great reduction in driving demand. The travel time reliability ratio is 0.50 and the value of each minute late is almost 5.0 times more than the average travel time with the restriction of the maximum allowed delays. The methods used in this study could be adopted to estimate the effect of variable pricing strategies on mode choice responses for different trip purposes. The high value given to travel time variability has implications for transport policy in terms of decision making with respect to new pricing strategies. Moreover, the valuation of travel time savings taken into account in this study would be helpful to better understand the effect of high parking fees.

Стилі APA, Harvard, Vancouver, ISO та ін.

48

Al-Kaissi, Zainab Ahmed. "Travel Time Prediction Models and Reliability Indices for Palestine Urban Road in Baghdad City." Al-Khwarizmi Engineering Journal 13, no. 3 (September 30, 2017): 120–30. http://dx.doi.org/10.22153/kej.2017.01.007.

Повний текст джерела

Анотація:

Abstract Travel Time estimation and reliability measurement is an important issues for improving operation efficiency and safety of traffic roads networks. The aim of this research is the estimation of total travel time and distribution analysis for three selected links in Palestine Arterial Street in Baghdad city. Buffer time index results in worse reliability conditions. Link (2) from Bab Al Mutham intersection to Al-Sakara intersection produced a buffer index of about 36% and 26 % for Link (1) Al-Mawall intersection to Bab Al- Mutham intersection and finally for link (3) which presented a 24% buffer index. These illustrated that the reliability get worst for link (2), (1) and (3) respectively during the peak period. Extra delay is observed on link(1), (2) and (3) in terms of 95% percentile travel time of about (301.9, 219.4, and 193.8)sec. for Link (1, 2 and 3) respectively. Higher value for 95% travel time is obtained for link (1). Travel time index (TTI) of 4.2 %, 4.9% and 4% is obtained for Link (1, 2 and 3) respectively. Maximum value for delay per km that obtained for link (1) which is about 266 sec/km and 268 sec./km for link (3) and 244 sec/km for link(2). Different predicted model for the three studied links of Palestine street have been developed based on the obtained field data. A best fit is presented as compared the predicted models with the observed field travel time data for all the models of studied links which illustrated that the predicted model can present the actual field data. Keywords: Delay, Buffer Index, Travel Time, predicted model, Reliability, Urban Arterial.

Стилі APA, Harvard, Vancouver, ISO та ін.

49

Miao, Xu, Bing Wu, Yajie Zou, and Lingtao Wu. "Examining the Impact of Different Periodic Functions on Short-Term Freeway Travel Time Prediction Approaches." Journal of Advanced Transportation 2020 (August 1, 2020): 1–15. http://dx.doi.org/10.1155/2020/3463287.

Повний текст джерела

Анотація:

Freeway travel time prediction is a key technology of Intelligent Transportation Systems (ITS). Many scholars have found that periodic function plays a positive role in improving the prediction accuracy of travel time prediction models. However, very few studies have comprehensively evaluated the impacts of different periodic functions on statistical and machine learning models. In this paper, our primary objective is to evaluate the performance of the six commonly used multistep ahead travel time prediction models (three statistical models and three machine learning models). In addition, we compared the impacts of three periodic functions on multistep ahead travel time prediction for different temporal scales (5-minute, 10-minute, and 15-minute). The results indicate that the periodic functions can improve the prediction performance of machine learning models for more than 60 minutes ahead prediction and improve the over 30 minutes ahead prediction accuracy for statistical models. Three periodic functions show a slight difference in improving the prediction accuracy of the six prediction models. For the same prediction step, the effect of the periodic function is more obvious at a higher level of aggregation.

Стилі APA, Harvard, Vancouver, ISO та ін.

50

Kuchipudi, Chandra Mouly, and Steven I. J. Chien. "Development of a Hybrid Model for Dynamic Travel-Time Prediction." Transportation Research Record: Journal of the Transportation Research Board 1855, no. 1 (January 2003): 22–31. http://dx.doi.org/10.3141/1855-03.

Повний текст джерела

Анотація:

Travel-time prediction has been an interesting research subject for decades, and various prediction models have been developed. A prediction model was derived by integrating path-based and link-based prediction models. Prediction results generated by the hybrid model and their accuracy are compared with those generated by the path-based and link-based models individually. The models were developed with real-time and historic data collected from the New York State Thruway by the Transportation Operations Coordinating Committee. In these models, the Kalman filtering algorithm is applied for travel-time prediction because of its significance in continuously updating the state variables as new observations. The experimental results reveal that the travel times predicted with the path-based model are better than those predicted with the link-based model during peak periods, and vice versa. The hybrid model derives results from the best model at a given time, thus optimizing the performance. A prototype prediction system was developed on the World Wide Web.

Стилі APA, Harvard, Vancouver, ISO та ін.

Ми пропонуємо знижки на всі преміум-плани для авторів, чиї праці увійшли до тематичних добірок літератури. Зв'яжіться з нами, щоб отримати унікальний промокод!