Analysis of Time Allocation, Departure Time, and Route Choice Behavior Under Congestion Pricing

2000 ◽  
Vol 1725 (1) ◽  
pp. 95-101 ◽  
Author(s):  
Toshiyuki Yamamoto ◽  
Satoshi Fujii ◽  
Ryuichi Kitamura ◽  
Hiroshi Yoshida
Keyword(s):  
Time Allocation ◽  
Choice Model ◽  
Stated Preference ◽  
Congestion Pricing ◽  
Route Choice ◽  
Multinomial Logit Model ◽  
Allocation Model ◽  
Departure Time ◽  
Departure Time Choice ◽  
Pricing Schemes

Driver behavior under congestion pricing is analyzed to evaluate the effects of alternative congestion pricing schemes. The analysis, which is based on stated preference survey results, focuses on time allocation, departure time choice, and route choice when a congestion pricing scheme is implemented on toll roads in Japan. A unique feature of the model system of this study is that departure time choice and route choice are analyzed in conjunction with the activities before and after the trip. A time allocation model is developed to describe departure time choice, and a route and departure time choice model is developed as a multinomial logit model with alternatives representing the choice between freeways and surface streets and, for departure time, the choice from among before, during, or after the period when congestion pricing is in effect. The results of the empirical analysis suggest that departing during the congestion pricing period tends to have higher utilities and that a worker and a nonworker have quite different utility functions. The comparative analysis of different congestion pricing schemes is carried out based on the estimated parameters. The results suggest that the probability of choosing each alternative is stable even if the length of the congestion pricing period changes, but a higher congestion price causes more drivers to change the departure time to before the congestion pricing period.

Analysis of Travel Time Saving Benefit by Understanding Individual Needs and Value of Activity Time: Case Study of Tokyo and Jakarta

2003 ◽  
Vol 1854 (1) ◽  
pp. 12-21 ◽  
Author(s):  
Irwan Prasetyo ◽  
Daisuke Fukuda ◽  
Hirosato Yoshino ◽  
Tetsuo Yai
Keyword(s):  
Travel Time ◽  
Time Allocation ◽  
Choice Model ◽  
Stated Preference ◽  
Revealed Preference ◽  
Psychological Needs ◽  
Activity Time ◽  
Time Saving ◽  
Allocation Model ◽  
Preference Data

Quantification of the value of time (VOT) is important for measurement of the benefit of transportation projects in terms of travel time savings. In Japan, VOT is considered higher on weekends than on weekdays because on the weekend people have limited time to allocate to discretionary activities that are not normally done on weekdays, such as family care-related activities. In Indonesia, a culturally diverse country, providers and users seem to have different perceptions of VOT. A method of analyzing the value of activity time is presented. It argues that the benefit of travel time saving should be evaluated in more detail on weekends by considering the value of discretionary activities to explain these phenomena theoretically. Activity diary surveys were conducted in Tokyo, Japan, and Jakarta, Indonesia, to verify the influence of psychological needs on people's holiday activities. Finally, a time allocation model that uses the revealed preference data and a marginal activity choice model that uses stated preference data are proposed to calculate the value of activity time. The theories underpinning these models are Maslow's psychological needs, consumer theory in economics, and a discrete choice model. The empirical results show that an individual's priority of needs influences time allocation. In particular, the results show that in Tokyo, spending time with family on weekends is more valuable than other types of activities, while in Indonesia the value of spending time with family exceeds that of work time even on weekdays.

scholarly journals Railway and road discrete choice model for foreign trade freight between Antioquia and the Port of Cartagena

2016 ◽  
Vol 36 (3) ◽  
pp. 22 ◽  
Author(s):  
Juan Diego Pineda Jaramillo ◽  
Iván Reinaldo Sarmiento Ordosgoitia ◽  
Jorge Eliécer Córdoba Maquilón
Keyword(s):  
Qualitative Research ◽  
Discrete Choice ◽  
Logit Model ◽  
Choice Model ◽  
Stated Preference ◽  
Revealed Preference ◽  
Freight Transportation ◽  
Multinomial Logit Model ◽  
Discrete Choice Model ◽  
The Cost

Most Colombian freight is transported on roads with barely acceptable conditions, and although there is a speculation about the need for a railway for freight transportation, there is not a study in Colombia showing the variables that influence the modal choice by the companies that generate freight transportation. This article presents the calculation of demand for a hypothetical railway through a discrete choice model. It begins with a qualitative research through focus group techniques to identify the variables that influence the choice of persons responsible for the transportation of large commercial companies in Antioquia (Colombia). The influential variables in the election were the cost and service frequency, and these variables were used to apply a Stated Preference (SP) and Revealed Preference (RP) survey, then to calibrate a Multinomial Logit Model (MNL), and to estimate the influence of each of them. We show that the probability of railway choice by the studied companies varies between 67% and 93%, depending on differences in these variables.

scholarly journals Can Road Toll Convince Car Travelers to Adjust Their Departure Times? Accounting for the Effect of Choice Behavior under Long and Short Holidays

2020 ◽  
Vol 12 (24) ◽  
pp. 10470
Author(s):  
Haiyan Zhu ◽  
Hongzhi Guan ◽  
Yan Han ◽  
Wanying Li
Keyword(s):  
Traffic Congestion ◽  
Choice Behavior ◽  
Road Traffic ◽  
Stated Preference ◽  
Revealed Preference ◽  
The Road ◽  
Departure Time ◽  
Departure Time Choice ◽  
Mnl Model ◽  
Peak Travel

The adjustment of road toll is an important measure that can alleviate road traffic congestion by convincing car travelers to travel during off-peak times. In order to reduce congestion on the expressway on the first day of a holiday, factors that affect the departure times of holiday travelers must be comprehensively understood to determine the best strategy to persuade car travelers to avoid peak travel times. This paper takes holiday car travelers as the research object and explores the characteristics and rules of departure time choice behavior for different holiday lengths. Based on Utility Maximization Theory, a multinomial logit (MNL) model of departure time choice for a three-day short holiday and a seven-day long holiday was established. Model calibration and elastic analysis were carried out using Revealed Preference/Stated Preference (RP/SP) survey data. Additionally, the influence of the highway toll policy on departure times for long and short holidays was analyzed. The results show that the rate of first-day departures is much higher than that of other departure times for both short and long vacations under the current policy of free holiday passage on highways. Factors such as trip duration, size of the tourist group, the number of visits, travel range, travel time, monthly income, occupation, age and road toll have a significant influence on the departure time decisions of holiday car travelers, and the effect and degree of influence are markedly different for different holiday lengths. The effects of tolls for each departure time and different pricing scenarios on the choice behavior of travelers are different between long and short holidays. Furthermore, the effectiveness of the road toll policy also varies for travelers with different travel distances. This study can provide useful information for the guidance of holiday travelers, the management of holiday tolls on expressways and the formulation of holiday leave time.

Dynamic Model Research of Departure Time and Route Choice Based on Prospect Theory

2011 ◽  
Vol 243-249 ◽  
pp. 4418-4421
Author(s):  
Zhi Yong Yang ◽  
Gui Yun Yan
Keyword(s):  
Prospect Theory ◽  
Choice Model ◽  
Weighting Function ◽  
Route Choice ◽  
Traffic Information ◽  
Traveler Information ◽  
Travel Choice ◽  
Traveler Information Systems ◽  
Model Research ◽  
Departure Time

This paper takes commuters’ daily travel as research object to build model of travel choice which contains departure time and travel route based on Prospect Theory. Choosing the time of arriving destination as reference point, commuter will choose the time at which he/she can obtain the maximum value as departure time, then establishes choice model of departure time. Using Bayesian Theory to update and adjust route’s forecasting travel time in light of traffic information provided by Advanced Traveler Information Systems (ATIS) and travelers’ previous experience information. Gets decision weighting function after having analyzed traveler’s individual subjective probability which is about the possible result for route choice, then obtains the expression of travel route’s prospect value and gets route choice model. Finally, by designing a network to analyze the dynamic choice model, and achieves expected effect.

scholarly journals Airport choice model in multiple airport regions

2017 ◽  
Vol 7 (1) ◽  
pp. 1 ◽  
Author(s):  
Claudia Muñoz ◽  
Jorge Cordoba ◽  
Iván Sarmiento
Keyword(s):  
Utility Function ◽  
Choice Model ◽  
Stated Preference ◽  
Multinomial Logit Model ◽  
Air Transportation ◽  
Future Market ◽  
Support Tool ◽  
Modeling Process ◽  
Market Shares ◽  
Crucial Component

Purpose: This study aims to analyze travel choices made by air transportation users in multi airport regions because it is a crucial component when planning passenger redistribution policies. The purpose of this study is to find a utility function which makes it possible to know the variables that influence users’ choice of the airports on routes to the main cities in the Colombian territory.Design/methodology/approach: This research generates a Multinomial Logit Model (MNL), which is based on the theory of maximizing utility, and it is based on the data obtained on revealed and stated preference surveys applied to users who reside in the metropolitan area of Aburrá Valley (Colombia). This zone is the only one in the Colombian territory which has two neighboring airports for domestic flights. The airports included in the modeling process were Enrique Olaya Herrera (EOH) Airport and José María Córdova (JMC) Airport. Several structure models were tested, and the MNL proved to be the most significant revealing the common variables that affect passenger airport choice include the airfare, the price to travel the airport, and the time to get to the airport.Findings and Originality/value: The airport choice model which was calibrated corresponds to a valid powerful tool used to calculate the probability of each analyzed airport of being chosen for domestic flights in the Colombian territory. This is done bearing in mind specific characteristic of each of the attributes contained in the utility function. In addition, these probabilities will be used to calculate future market shares of the two airports considered in this study, and this will be done generating a support tool for airport and airline marketing policies.

scholarly journals A Bayesian Network Modeling for Departure Time Choice: A Case Study of Beijing Subway

2018 ◽  
Vol 30 (5) ◽  
pp. 579-587 ◽  
Author(s):  
Xian Li ◽  
Haiying Li ◽  
Xinyue Xu
Keyword(s):  
Bayesian Network ◽  
Stated Preference ◽  
Discrete Choice Models ◽  
Model Parameters ◽  
Time Saving ◽  
Morning Peak ◽  
Departure Time ◽  
Departure Time Choice ◽  
Beijing Subway

Departure time choice is critical for subway passengers to avoid congestion during morning peak hours. In this study, we propose a Bayesian network (BN) model to capture departure time choice based on data learning. Factors such as travel time saving, crowding, subway fare, and departure time change are considered in this model. K2 algorithm is then employed to learn the BN structure, and maximum likelihood estimation (MLE) is adopted to estimate model parameters, according to the data obtained by a stated preference (SP) survey. A real-world case study of Beijing subway is illustrated, which proves that the proposed model has higher prediction accuracy than typical discrete choice models. Another key finding indicates that subway fare discount higher than 20% will motivate some passengers to depart 15 to 20 minutes earlier and release the pressure of crowding during morning peak hours.

scholarly journals A Dynamic Day-To-Day Departure Time and Route Choice Model for Bounded-Rational Individuals

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Lingjuan Chen ◽  
Yu Wang ◽  
Dongfang Ma
Keyword(s):  
Real Time ◽  
Traffic Management ◽  
Choice Model ◽  
Route Choice ◽  
User Equilibrium ◽  
Navigation Systems ◽  
Dynamic Learning ◽  
Traffic State ◽  
Departure Time ◽  
Navigation Information

Accurate prediction of travellers’ day-to-day departure time and route choice is critical in advanced traffic management systems. There have been several related works about route choice with the assumption that the departure time for individual travellers is known beforehand. With real-time traffic state information provided by navigation systems and previous historical experience, travellers will dynamically update their departure time, which is neglected in existing works. In this study, we aim to describe travellers’ spatial-temporary choice behaviour taking navigation information into account and propose a bounded-rational day-to-day dynamic learning and adjustment model. The new model contains three steps. First, the real-time navigation guidance on each discrete day is obtained, and the self-learned experience of travellers’ choices with navigation information is presented; then, the day-to-day revision process of the choices is derived to maximize departure and route choice prospect; next, by aggregating each individual’s behaviour and calculating route choice probability, a bounded-rational continuous day-to-day dynamic model is provided. Numerical experiments suggest that the proposed model converges to a spatial-temporal oscillating equilibrium not a fixed-point stable status, and the final equilibrium trend is different from classical user equilibrium. The findings of the study are helpful to improve the prediction accuracy of traffic state in urban street networks.

scholarly journals ARRIVAL TIME VALUATION OF COMMUTERS IN URBAN RAIL TRANSIT

Transport ◽  
2019 ◽  
Vol 34 (3) ◽  
pp. 383-393
Author(s):  
Yan Cheng ◽  
Xiafei Ye ◽  
Zhi Wang
Keyword(s):  
Reference Point ◽  
Arrival Time ◽  
Choice Model ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Value Functions ◽  
Schedule Delay ◽  
Urban Rail ◽  
Departure Time ◽  
Departure Time Choice

Departure time choice of commuters is one of key decisions affecting the crowding of urban rail transit network during peak hours. It is influenced by arrival time value, the additional psychological pressure caused by in-vehicle crowding, and time uncertainty. This paper aims at investigating how commuters in urban rail transit value their arrival time at work/school. Three valuation frameworks are proposed based on the reference point approach of prospect theory. Non-linear value functions with different reference point alternatives are estimated using data from a survey and stated choice study of users of Shanghai Metro system. Results show that schedule delay with work/school start time as the only reference point cannot properly reflect the arrival time valuation of urban rail transit commuters. Instead, the valuation framework with preferred arrival time as a reference point fits best, which hits as much as 85.64% of the cases. The asymmetrical response to early-side and late-side arrivals is identified. The findings of this study provide an essential basis for the development of departure time choice model.

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