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Journal articles on the topic 'Travel Mode Choice'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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1

Bamberg, Sebastian, and Peter Schmidt. "CHANGING TRAVEL-MODE CHOICE AS RATIONAL CHOICE:." Rationality and Society 10, no. 2 (1998): 223–52. http://dx.doi.org/10.1177/104346398010002005.

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2

Matthies, Ellen, Silke Kuhn, and Christian A. Klöckner. "Travel Mode Choice of Women." Environment and Behavior 34, no. 2 (2002): 163–77. http://dx.doi.org/10.1177/0013916502034002001.

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3

Cheng, Long, Xuewu Chen, Ming Wei, Jingxian Wu, and Xianyao Hou. "Modeling Mode Choice Behavior Incorporating Household and Individual Sociodemographics and Travel Attributes Based on Rough Sets Theory." Computational Intelligence and Neuroscience 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/560919.

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Most traditional mode choice models are based on the principle of random utility maximization derived from econometric theory. Alternatively, mode choice modeling can be regarded as a pattern recognition problem reflected from the explanatory variables of determining the choices between alternatives. The paper applies the knowledge discovery technique of rough sets theory to model travel mode choices incorporating household and individual sociodemographics and travel information, and to identify the significance of each attribute. The study uses the detailed travel diary survey data of Changxi

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Sidharthan, Raghuprasad, Chandra R. Bhat, Ram M. Pendyala, and Konstadinos G. Goulias. "Model for Children's School Travel Mode Choice." Transportation Research Record: Journal of the Transportation Research Board 2213, no. 1 (2011): 78–86. http://dx.doi.org/10.3141/2213-11.

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5

Wu, Chaoqun, Yulong Pei, and Jingpeng Gao. "Evolution Game Model of Travel Mode Choice in Metropolitan." Discrete Dynamics in Nature and Society 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/638972.

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The paper describes an evolution game model of travel mode choice to determine whether transportation policies would have the desired effect. The model is first expressed as a two-stage sequential game in the extensive form based on the similarity between evolution game theory and the travel mode choice process. Second, backward induction is used to solve for Nash equilibrium of the game based on the Folk Theorem. Third, the sensitivity analysis suggests that a payoff reduction of travel by any mode will result in a rising proportion of inhabitants travelling by that mode and falling proportio

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6

Hagberg, Johan, and Ulrika Holmberg. "Travel modes in grocery shopping." International Journal of Retail & Distribution Management 45, no. 9 (2017): 991–1010. http://dx.doi.org/10.1108/ijrdm-08-2016-0134.

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Purpose Although the movement of goods by consumers represents a large proportion of the economic and environmental impact of the distribution chain, this topic has been insufficiently explored in the retailing literature. The purpose of this paper is to contribute to the understanding of shopping travel-mode choice in the context of grocery shopping. Design/methodology/approach The paper presents findings from a Swedish national survey of 1,694 respondents that included questions regarding travel-mode choices and consumer characteristics, mobility conditions, shopping behaviours and environme

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Wang, Yanli, Bing Wu, Zhi Dong, and Xin Ye. "A Joint Modeling Analysis of Passengers’ Intercity Travel Destination and Mode Choices in Yangtze River Delta Megaregion of China." Mathematical Problems in Engineering 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/5293210.

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Joint destination-mode travel choice models are developed for intercity long-distance travel among sixteen cities in Yangtze River Delta Megaregion of China. The model is developed for all the trips in the sample and also by two different trip purposes, work-related business and personal business trips, to accommodate different time values and attraction factors. A nested logit modeling framework is applied to model trip destination and mode choices in two different levels, where the lower level is a mode choice model and the upper level is a destination choice model. The utility values from v

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Chen, Hong, Zuo-xian Gan, and Yu-ting He. "Choice Model and Influencing Factor Analysis of Travel Mode for Migrant Workers: Case Study in Xi’an, China." Discrete Dynamics in Nature and Society 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/236216.

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Based on the basic theory and methods of disaggregate choice model, the influencing factors in travel mode choice for migrant workers are analyzed, according to 1366 data samples of Xi’an migrant workers. Walking, bus, subway, and taxi are taken as the alternative parts of travel modes for migrant workers, and a multinomial logit (MNL) model of travel mode for migrant workers is set up. The validity of the model is verified by the hit rate, and the hit rates of four travel modes are all greater than 80%. Finally, the influence of different factors affecting the choice of travel mode is analyze

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Miller, Eric J., Matthew J. Roorda, and Juan Antonio Carrasco. "A tour-based model of travel mode choice." Transportation 32, no. 4 (2005): 399–422. http://dx.doi.org/10.1007/s11116-004-7962-3.

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10

Li, Ming, Guohua Song, Ying Cheng, and Lei Yu. "Identification of Prior Factors Influencing the Mode Choice of Short Distance Travel." Discrete Dynamics in Nature and Society 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/795176.

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Short distance trips are defined as any trips shorter than or equal to 5 kilometers, which have been found to be a big contributor to the traffic congestion problem. This paper is intended to analyze factors that influence the mode choice of short distance travels in order to help reduce short distance trips by cars. A survey is conducted at two typical kinds of residential areas, one with a high proportion of short distance car trips and another one with a low proportion. Then, by applying the structural equation modeling, it is found that the age, the household income, and the vehicle owners

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Ding, Chuan, Yu Chen, Jinxiao Duan, Yingrong Lu, and Jianxun Cui. "Exploring the Influence of Attitudes to Walking and Cycling on Commute Mode Choice Using a Hybrid Choice Model." Journal of Advanced Transportation 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/8749040.

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Transport-related problems, such as automobile dependence, traffic congestion, and greenhouse emissions, lead to a great burden on the environment. In developing countries like China, in order to improve the air quality, promoting sustainable travel modes to reduce the automobile usage is gradually recognized as an emerging national concern. Though there are many studies related to the physically active modes (e.g., walking and cycling), the research on the influence of attitudes to active modes on travel behavior is limited, especially in China. To fill up this gap, this paper focuses on exam

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12

Liu, Xia, and Jian Lu. "Gender Differences in Travel Mode Choice Behavior in Zhenfeng City." Applied Mechanics and Materials 361-363 (August 2013): 1906–9. http://dx.doi.org/10.4028/www.scientific.net/amm.361-363.1906.

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Gender difference is an important factor in travel mode choice behavior. In this paper, some characteristics of different travelers were found from a survey of Zhenfeng City. Based on the data, this paper developed MNL models about four main travel mode choices (walk, bus, car and motorcycle) of different gender, and six variables were used in the models. Overall, the models represented the gender differences in travel mode choice, and it was influenced by a wide variety of variables, including age, employment status, household income, number of cars, number of motorcycles and travel purpose.

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Lu, Ming, Hai Zhu, Xia Luo, and Lei Lei. "Intercity Travel Demand Analysis Model." Advances in Mechanical Engineering 6 (January 1, 2014): 108180. http://dx.doi.org/10.1155/2014/108180.

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It is well known that intercity travel is an important component of travel demand which belongs to short distance corridor travel. The conventional four-step method is no longer suitable for short distance corridor travel demand analysis for the time spent on urban traffic has a great impact on traveler's main mode choice. To solve this problem, the author studied the existing intercity travel demand analysis model, then improved it based on the study, and finally established a combined model of main mode choice and access mode choice. At last, an integrated multilevel nested logit model struc

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Nurul Habib, Khandker. "A Heteroscedastic Polarized Logit Model to Investigate the Competition of Bicycle for the Bikeable Trips with the Other Modes." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 49 (2018): 58–68. http://dx.doi.org/10.1177/0361198118796357.

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The paper proposes a new discrete choice model, named the Heteroscedastic Polarized Logit (HPL) to investigate choice contexts with one or more alternatives with remarkably low market shares. The proposed model is used to investigate the factors influencing the choice of a bicycle as a travel mode in the National Capital Region (NCR) of Canada. Data from the latest household travel survey of the NCR are used to investigate the mode choices of bikeable trips. Bikeable trips are defined as trips with lengths shorter than 16 km as this is the observed maximum limit of a bicycle trip in the datase

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Haggar, Paul, Lorraine Whitmarsh, and Stephen M. Skippon. "Habit discontinuity and student travel mode choice." Transportation Research Part F: Traffic Psychology and Behaviour 64 (July 2019): 1–13. http://dx.doi.org/10.1016/j.trf.2019.04.022.

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Klein, Nicholas J., and Michael J. Smart. "Travel mode choice among same-sex couples." Transportation Research Part A: Policy and Practice 90 (August 2016): 1–13. http://dx.doi.org/10.1016/j.tra.2016.05.009.

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Moeckel, Rolf, Rhett Fussell, and Rick Donnelly. "Mode choice modeling for long-distance travel." Transportation Letters 7, no. 1 (2014): 35–46. http://dx.doi.org/10.1179/1942787514y.0000000031.

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18

Zhang, Rui, Enjian Yao, and Zhili Liu. "School travel mode choice in Beijing, China." Journal of Transport Geography 62 (June 2017): 98–110. http://dx.doi.org/10.1016/j.jtrangeo.2017.06.001.

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Sims, Dangaia, and Melissa Bopp. "Examining Travel Mode Choice in Underserved Populations." Medicine & Science in Sports & Exercise 47 (May 2015): 727. http://dx.doi.org/10.1249/01.mss.0000478710.31650.41.

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Lee, Chanam, Xuemei Zhu, Jeongjae Yoon, and James W. Varni. "Beyond Distance: Children’s School Travel Mode Choice." Annals of Behavioral Medicine 45, S1 (2013): 55–67. http://dx.doi.org/10.1007/s12160-012-9432-z.

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Prasad, Bollini, and Kumar Molugaram. "Development of mode choice models of a trip maker for Hyderabad metropolitan city." International Journal of Engineering & Technology 7, no. 1.6 (2018): 1. http://dx.doi.org/10.14419/ijet.v7i1.6.9014.

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The rapid development of urbanization, population growth and the rapid development of economy resulted in the rapid increase in the total number of motor vehicles in the modern cities of India. Consequently, the importance of forecasting of the travel demand model has been increased in the recent years. Forecasting of the travel demand model involves various stages of trip generation and distribution, mode choice and traffic assignment. Among these stages, the mode choice analysis is a prominent stage as it considers the travelers mode to reach their destination. Further, study of mode choice

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22

Deng, Yajuan, and Mingli Chen. "Impacts of Real-Time Transit Information on Bus Passengers’ Travel Choices Based on Travel Behaviour Survey." Promet - Traffic&Transportation 33, no. 4 (2021): 539–50. http://dx.doi.org/10.7307/ptt.v33i4.3637.

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Real-time transit information (RTI) service can provide travellers with information on public transport and guide them to arrange departure time and travel mode accordingly. This paper aims to analyse travellers’ choices under RTI by exploring the relationship between the related variables of RTI and passengers’ travel choice. Based on the stated preference (SP) survey data, the ordinal logistic regression model is established to analyse the changing probability of passengers’ travel behaviour under RTI. The model calculation results show that travellers getting off work are more likely to cha

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23

Mansyur, Resdiansyah, and Fredy Jhon Philip.S. "Travel Time Cost for Private Car in Bumi Serpong Damai (BSD) City: Income and Mode Choice Approach." WIDYAKALA JOURNAL 4, no. 2 (2017): 99. http://dx.doi.org/10.36262/widyakala.v4i2.62.

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Abstract : Travel time cost is one of the primary aspects that considered, especially with respect to its economic impact. To account for the increment of road users, often travel time cost has to be highlighted and further investigated. One technique to compute travel time costs is by using travel time evaluation as a loss opportunity that used to produce traveling needs. The aim of the study is to compute travel time cost for the private transportation users in BSD City by employing two methodologies. Income approach is the first method, which requires only two factors to be computed (capita

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24

Nerhagen, Lena. "Travel Mode Choice: Effects of Previous Experience on Choice Behaviour and Valuation." Tourism Economics 9, no. 1 (2003): 5–30. http://dx.doi.org/10.5367/000000003101298240.

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This paper investigates how past experience influences choice behaviour and valuation in a hypothetical travel mode choice situation. Using a stated choice question asked of visitors to a major ski resort in Sweden, the author explores whether an individual's choice behaviour, when he or she is offered a comfort improvement to train travel, can be explained with reference to the individual and to the circumstances of his or her previous journey. The analysis models and compares the response behaviour of travellers who used a car and travellers who used the train on their original trip. It is f

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Chen, Ruijing. "Simulation Modeling and Application of Travel Mode Choice Based on Bayesian Network." Open Mechanical Engineering Journal 8, no. 1 (2014): 19–25. http://dx.doi.org/10.2174/1874155x01408010019.

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In this paper, we study the travel mode choice of residents to determine the set of factors which can influence travel mode choice of residents and analyze the influence factor characteristics. Using Bayesian theory, we analyze the travel decision-making data of the residents, discrete them, and use them in Bayesian network structure learning and parameter estimation by K2 algorithm. We establish a Bayesian network simulation model to analyze the dependence probability relationship between the parent nodes and child nodes. Validation test was carried out for the building simulation model of Ba

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26

Davidov, Eldad, Kajsa Yang-Hansen, Jan-Eric Gustafsson, Peter Schmidt, and Sebastian Bamberg. "Does Money Matter? A Theory-Driven Growth Mixture Model to Explain Travel-Mode Choice with Experimental Data." Methodology 2, no. 3 (2006): 124–34. http://dx.doi.org/10.1027/1614-2241.2.3.124.

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In the present article we apply a growth mixture model using Mplus via STREAMS to delineate the mechanism underlying travel-mode choice. Three waves of an experimental field study conducted in Frankfurt Main, Germany, are applied for the statistical analysis. Five major questions are addressed: (1) whether the choice of public transport rather than the car changes over time; (2) whether a soft policy intervention to change travel mode choice has any effect on the travel-mode chosen; (3) whether one can identify different groups of people regarding the importance allocated to monetary and time

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Abdelwahab, Walid M., J. David Innes, and Albert M. Stevens. "Development of disaggregate mode choice models of intercity travel in Canada." Canadian Journal of Civil Engineering 19, no. 6 (1992): 965–74. http://dx.doi.org/10.1139/l92-115.

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This paper reports and discusses the results of an effort to develop disaggregate behavioral mode choice models of intercity travel in Canada. Currently available data bases of intercity travel in Canada are reviewed. The feasibility of using data from national travel surveys to develop statistically reliable intercity mode choice models is examined, and directions for future disaggregate data collection efforts are offered. The models developed are of the multinomial logit (MNL) type which included all intercity passenger travel modes: auto, air, bus, and rail. For purposes of estimation, the

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Zhang, Quan, and Juan Li. "Self-Organized Critical Condition of Travel Mode Choice Model Analysis." Advanced Materials Research 1030-1032 (September 2014): 2235–39. http://dx.doi.org/10.4028/www.scientific.net/amr.1030-1032.2235.

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By studying the service property of different travel modes, the self-organization theory presented in this paper to research the self-organized criticality, highlighting by the discovery and description of self-organized critical condition of travel mode choice, is of inspiring importance. The state equation and critical property analysis proposed in the paper is validated by practical example in Macao.

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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.

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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 wer

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Liu, Yue, Jun Chen, Weiguang Wu, and Jiao Ye. "Typical Combined Travel Mode Choice Utility Model in Multimodal Transportation Network." Sustainability 11, no. 2 (2019): 549. http://dx.doi.org/10.3390/su11020549.

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The primary purpose of this paper is to explore the mechanism of combined travel mode choice in multimodal networks. To meet the objective, stated preference survey and revealed preference survey are designed under short, middle, and long travel distance scenarios. Data including travelers’ socio-economic/personal information, trip characteristics, and mode choice are collected and analyzed. To recognize the influential factors of mode choice, a nested logit model is established. A value of time estimation and sensitivity analysis are conducted to quantify the influencing degree. The results r

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Mahmoud, Mohamed S., Khandker M. Nurul Habib, and Amer Shalaby. "Survey of Cross-Regional Intermodal Passenger Travel." Transportation Research Record: Journal of the Transportation Research Board 2526, no. 1 (2015): 108–18. http://dx.doi.org/10.3141/2526-12.

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This paper presents an investigation of the mode choice behavior of cross-regional commuters in the greater Toronto and Hamilton area of Ontario, Canada. A survey of cross-regional intermodal passenger travel (called SCRIPT) was developed and conducted during the spring and the fall of 2014. SCRIPT collects data on respondents' revealed preference in daily commuting trips to pivot each respondent's mode choice stated preference experiment separately. An innovative multimodal trip planner tool was developed to generate feasible travel options for each stated preference experiment with informati

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Liu, Xiaoning, Linjie Gao, Anning Ni, and Nan Ye. "Understanding Better the Influential Factors of Commuters’ Multi-Day Travel Behavior: Evidence from Shanghai, China." Sustainability 12, no. 1 (2020): 376. http://dx.doi.org/10.3390/su12010376.

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Alleviating traffic congestion and developing sustainable transportation systems in a city can be assisted by promoting environmentally friendly transportation modes such as walking, cycling, and public transport. Strategies for promoting these desirable transportation modes can be identified based on a sound understanding of how commuters choose travel modes. In this study, multi-day commuting travel mode data was used to explore factors that influenced commute mode choice. A multinomial logit model and a binary logit model were proposed to study commuter travel behavior. The results showed t

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Al-Salih, Wissam Qassim, and Domokos Esztergár-Kiss. "Linking Mode Choice with Travel Behavior by Using Logit Model Based on Utility Function." Sustainability 13, no. 8 (2021): 4332. http://dx.doi.org/10.3390/su13084332.

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The currently available transport modeling tools are used to evaluate the effects of behavior change. The aim of this study is to analyze the interaction between the transport mode choice and travel behavior of an individual—more specifically, to identify which of the variables has the greatest effect on mode choice. This is realized by using a multinomial logit model (MNL) and a nested logit model (NL) based on a utility function. The utility function contains activity characteristics, trip characteristics including travel cost, travel time, the distance between activity place, and the indivi

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Zhong, Jiayu, Xin Ye, Ke Wang, and Dongjin Li. "A Modeling Analysis of Impact from E-Hailing Service on Non-Work Travel Mode in Shanghai, China." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 47 (2018): 125–34. http://dx.doi.org/10.1177/0361198118792324.

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With the rapid development of mobility services, e-hailing service have been highly prevalent and e-hailing travel has become a part of daily life in many cities in China. At the same time, travelers’ mode choice behaviors have been influenced to some degree by different factors, and in this paper, a web-based retrospective survey initially conducted in Shanghai, China is used to analyze the extent to which various factors are influencing mode choice behaviors. Then, a multinomial-logit-based mode choice model is developed to incorporate the e-hailing auto mode as a new travel mode for non-wor

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Indriany, Sylvia, Ade Sjafruddin, Aine Kusumawati, and Widyarini Weningtyas. "Identification of cumulative prospect theory parameters for mode choice model." MATEC Web of Conferences 270 (2019): 03012. http://dx.doi.org/10.1051/matecconf/201927003012.

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The use of Cumulative Prospect Theory (CPT) in decision making related to transportation risk is still much debated. Mainly because of the travel and socio-economic characteristics of the traveller it possible for different responses to the specified Reference Point (RP) as well as the loss aversion. This difference can be seen from the value of Cumulative Prospect Theory parameters. Therefore, this paper will discuss about the determination of parameters CPT which affect public transportation mode choice model in the course of work trip activity. The reference point as an essential part of th

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Wang, Fangru, and Catherine L. Ross. "Machine Learning Travel Mode Choices: Comparing the Performance of an Extreme Gradient Boosting Model with a Multinomial Logit Model." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 47 (2018): 35–45. http://dx.doi.org/10.1177/0361198118773556.

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The multinomial logit (MNL) model and its variations have been dominating the travel mode choice modeling field for decades. Advantages of the MNL model include its elegant closed-form mathematical structure and its interpretable model estimation results based on random utility theory, while its main limitation is the strict statistical assumptions. Recent computational advancement has allowed easier application of machine learning models to travel behavior analysis, though research in this field is not thorough or conclusive. In this paper, we explore the application of the extreme gradient b

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Fang, Xiaoping, Yajing Xu, and Weiya Chen. "Understanding Attitudes towards Proenvironmental Travel: An Empirical Study from Tangshan City in China." Computational Intelligence and Neuroscience 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/963683.

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Understanding people’s attitudes towards proenvironmental travel will help to encourage people to adopt proenvironmental travel behavior. Revealed preference theory assumes that the consumption preference of consumers can be revealed by their consumption behavior. In order to investigate the influences on citizens’ travel decision and analyze the difficulties of promoting proenvironmental travel behavior in medium-sized cities in China, based on revealed preference theory, this paper uses the RP survey method and disaggregate model to analyze how individual characteristics, situational factors

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Krishnapriya, M. G. "Mode choice behaviour of students, integrating residential location characteristics: a study from Kochi City, India." European Transport/Trasporti Europei 79, ET.2020 (2020): 1–17. http://dx.doi.org/10.48295/et.2020.79.5.

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Mode choice decision of individuals plays a vital role in transportation planning. Individual travel behaviour models can be improved by extending the set of influencing variables used for modelling. In a developing country like India, students contribute a major share of total travel demand especially during morning and evening peak hours of traffic; whose individual travel characteristics are very less studied by transportation professionals. This paper presents exploratory and statistical analysis of mode choice behaviour of students in Kochi City, India. The socio-demographic characteristi

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Outwater, Maren L., Steve Castleberry, Yoram Shiftan, Moshe Ben-Akiva, Yu Shuang Zhou, and Arun Kuppam. "Attitudinal Market Segmentation Approach to Mode Choice and Ridership Forecasting: Structural Equation Modeling." Transportation Research Record: Journal of the Transportation Research Board 1854, no. 1 (2003): 32–42. http://dx.doi.org/10.3141/1854-04.

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The San Francisco Bay Area Water Transit Authority is evaluating expanded ferry service, as required by the California Legislature. As part of this process, Cambridge Systematics developed forecasts using a combination of market research strategies and the addition of nontraditional variables into the mode choice modeling process. The focus of this work was on expanding the mode choice model to recognize travelers' attitudes and different market segments. Structural equation modeling was used to simultaneously identify the attitudes of travel behaviors and the causal relationships between trav

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40

Eash, Ronald. "Destination and Mode Choice Models for Nonmotorized Travel." Transportation Research Record: Journal of the Transportation Research Board 1674, no. 1 (1999): 1–8. http://dx.doi.org/10.3141/1674-01.

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Zhang, Yunlong, and Yuanchang Xie. "Travel Mode Choice Modeling with Support Vector Machines." Transportation Research Record: Journal of the Transportation Research Board 2076, no. 1 (2008): 141–50. http://dx.doi.org/10.3141/2076-16.

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Halat, Hooram, Meead Saberi, Charlotte Anne Frei, Andreas Rolf Frei, and Hani S. Mahmassani. "Impact of Crime Statistics on Travel Mode Choice." Transportation Research Record: Journal of the Transportation Research Board 2537, no. 1 (2015): 81–87. http://dx.doi.org/10.3141/2537-09.

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Whether crime or the perception of it has any direct and significant influence on travelers’ mode choice is a topic for which the evidence remains inconclusive. Studies have revealed various, and in some cases counterintuitive, roles that safety concerns can play in individuals’ travel behavior. In addition, characteristics of the physical environment such as land use and walkability are also influential factors in travelers’ decisions. This study explored these questions through the study of individual travel behavior by using discrete choice models applied to the reported home-based work tri

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A, Miskinyte, Dedele A, Andrusaityte S, and Nemaniute-Guziene J. "Impact of commuting distance on travel mode choice." Environmental Epidemiology 3 (October 2019): 272. http://dx.doi.org/10.1097/01.ee9.0000608904.80486.31.

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Sweet, Matthias N., and Mengke Chen. "Does regional travel time unreliability influence mode choice?" Transportation 38, no. 4 (2011): 625–42. http://dx.doi.org/10.1007/s11116-011-9335-z.

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LI, Bing, and Hiroyuki TAMURA. "Commute Travel Mode Choice Logit Model with Imprecision Explanatory Variable." Transactions of the Institute of Systems, Control and Information Engineers 13, no. 8 (2000): 353–59. http://dx.doi.org/10.5687/iscie.13.8_353.

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Li, Juan, Jinxian Weng, Chunfu Shao, and Hongwei Guo. "Cluster-Based Logistic Regression Model for Holiday Travel Mode Choice." Procedia Engineering 137 (2016): 729–37. http://dx.doi.org/10.1016/j.proeng.2016.01.310.

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47

Bhat, Chandra R. "A heteroscedastic extreme value model of intercity travel mode choice." Transportation Research Part B: Methodological 29, no. 6 (1995): 471–83. http://dx.doi.org/10.1016/0191-2615(95)00015-6.

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Kim, Eui-Jin. "Analysis of Travel Mode Choice in Seoul Using an Interpretable Machine Learning Approach." Journal of Advanced Transportation 2021 (March 1, 2021): 1–13. http://dx.doi.org/10.1155/2021/6685004.

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Understanding choice behavior regarding travel mode is essential in forecasting travel demand. Machine learning (ML) approaches have been proposed to model mode choice behavior, and their usefulness for predicting performance has been reported. However, due to the black-box nature of ML, it is difficult to determine a suitable explanation for the relationship between the input and output variables. This paper proposes an interpretable ML approach to improve the interpretability (i.e., the degree of understanding the cause of decisions) of ML concerning travel mode choice modeling. This approac

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Dehghani, Youssef, Thomas Adler, Michael W. Doherty, and Randy Fox. "Development of a New Toll Mode-Choice Modeling System for Florida’s Turnpike Enterprise." Transportation Research Record: Journal of the Transportation Research Board 1858, no. 1 (2003): 9–17. http://dx.doi.org/10.3141/1858-02.

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The Florida Department of Transportation Turnpike Enterprise’s recent toll mode-choice model development activities are described. Because the simple toll travel forecasting analysis methods used were not adequate for reliably addressing contemporary toll study issues, there was a need for toll modeling innovations that address trip makers’ toll route decisions as a mode-choice step sensitive to changes in service levels by time of day, trip purpose, and socioeconomic attributes. Innovations developed for Florida’s turnpike began with data-collection efforts and toll model development for the

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Liu, Wen Zhi, Gang Zong, and Lian Yuan Cheng. "Analysis on Relationship of Mode Choice and Residential Location." Advanced Materials Research 616-618 (December 2012): 1231–33. http://dx.doi.org/10.4028/www.scientific.net/amr.616-618.1231.

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The relationship between urban transportation and land use is always an endless main topic in urban planning area. It is based on the theory of Nested Logit model, using the data of daily travel revealed preference survey of Beijing in 2008, set up the integrated model of mode choice and land use in this paper. The model have two levels, the first one is space choice level, which is using the land use variables, such as residential location and mixed land use; the other is mode choice level, taking the transportation variables, like travel time, travel cost etc. Parameter estimation results sh

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