Departure Time Choice Behavior Research Articles

Pre-peak fare discount policy for managing morning peak demand of interregional bus travel: a case study in Seoul metropolitan area

ABSTRACT Pre-peak fare discount policies have become increasingly common in urban transit services and show potential to spread morning peak-hour demand. This study analyzes the departure time choice behavior of interregional bus passengers during morning hours. A mixed logit model is applied to estimate the probability of shifting departure time as a function of fare discount rate, in-vehicle congestion, and other influential factors, accommodating heterogeneity of passenger preferences. The analysis data are obtained through a stated preference survey, which is conducted to passengers who regularly use the interregional bus in Seoul metropolitan area. Choice models are segmented by departure time periods (peak, pre-peak, and post-peak) and occupation types (fixed time worker, flexible time worker, and student). Marginal utilities of the unobserved preferences are estimated to suggest policy implications. From the experimental analysis, target passengers for shifting departure time during peak hour are suggested as flexible time workers and students.

Experimental study on the departure time choice behavior during the morning rush hours under different road capacity conditions

ABSTRACT This paper presents an experimental study on the departure time choice behavior during the morning rush hours under different road capacity conditions. Experimental data are analyzed from the aspects of the equilibrium state of a traffic system and the choice behavior of subjects. The experimental results showed that the user equilibrium is easy to achieve in the medium-capacity scenario; however, it is difficult in the low- and high-capacity scenario. This implies that the user equilibrium cannot predict the aggregate behavior well when the bottleneck capacity is too low or too high. A reinforcement learning model is constructed to reproduce experimental results and uncover subjects’ learning mechanism. Simulation results are in good agreement with the experimental results. The results presented in this study could provide the theoretical support for developing measures for transportation management and control during the morning rush hours.

Commuter departure time choice behavior under congestion charge: Analysis based on cumulative prospect theory

Commuter departure time choice behavior under congestion charge: Analysis based on cumulative prospect theory

Understanding the behavioral effect of incentives on departure time choice using inverse reinforcement learning

Encouraging departure time shifts could relieve peak-hour congestion. To understand departure time choice behavior is of great importance to evaluate the effectiveness of Travel Demand Management (TDM) instruments, such as pricing and incentive. Previous studies on the departure time choice behavior modeling can be generally divided into random-utility discrete choice models and machine learning models. In this work, we conduct a laboratory virtual experiment as a cheap approach to collect real person’s departure time choice data. An Inverse Reinforcement Learning (IRL) method is proposed to capture travelers’ preferences to times of departure. In IRL, the weights of the reward function are used to depict the departure time choice behavior based on observed data, so as to rationalize the strategy of real travelers. The results show that the IRL model can reproduce individual choice behavior. With the pricing and incentive schemes, travelers shift their departure times significantly. Based on the obtained reward function, the departure time choices after modifying the control measure could be predicted. The famous psychologic phenomenon of loss aversion is also observed in the IRL model.

Experimental study and modeling of departure time choice behavior in the bottleneck model with staggered work hours

This paper designed an experiment to explore the effects of staggered work hours on the departure time choice behavior of commuters based on a discrete Vickrey’s bottleneck model. The experiment included a benchmark scenario with one work start time and a treatment scenario with two work start times. In the treatment scenario, subjects were classified into two groups with different work start times, and such two groups had interactions because of the small gap between the two work start times. Our experimental results indicated that (i) subjects’ choice behavior deviated from the pure-strategy equilibrium in both scenarios, (ii) subjects’ average travel cost in the benchmark scenario was higher than that of the pure-strategy equilibrium, and (iii) in the treatment scenario, the average travel cost in the group with earlier work start time did not significantly differ from the pure-strategy equilibrium, whereas subjects in the group with later work start time had lower average travel cost than the pure-strategy equilibrium. Finally, we proposed a reinforcement learning model to simulate the departure time choice behavior of the commuters. The simulation results were consistent with the collective behavior of the experimental observations. Our work is expected to shed light on the theoretical study on the departure time choice behavior concerning staggered work hours.

Departure time choice behavior in commute problem with stochastic bottleneck capacity: experiments and modeling

ABSTRACT The effect of environmental uncertainty on equilibrium patterns is of vital importance to understand travel choice behavior. This paper conducted a laboratory experiment to investigate the effects of stochastic bottleneck capacity on departure time choice behavior. In the experiment, the bottleneck capacity varied stochastically from round to round, and two different scenarios with different information feedback were investigated. Our experimental results showed that the relationship between the mean travel cost and the standard deviation of travel cost on each departure time was fitted approximately linearly with a positive slope , indicating subjects were more likely to minimize their travel cost budget rather than their mean travel cost. Also, we found that the feedback on costs of all departure times resulted in a smaller than the feedback on the subjects’ own travel cost only. We propose a reinforcement learning model to reproduce the main experimental findings.

Incorporating active and non-active mode preferences for Departure time choice behavior

ABSTRACT This paper presents the joint analysis of departure time choice for non-work activities and latent preferences toward the active and non-active mode. Utilizing the concept of smart growth design of neighborhoods, scheduling the travel activities considering active transport through smart phone applications is one of the efficient ways to promote active mode usage. The hypothesis of the current study states that departure time decision-making for non-work activities is influenced by disposition toward active modes such as cycling in addition to the socio-economic factors and dependency on mandatory activities. Information from this model is expected to provide the empirical support to the policies regarding the promotion of active mode usage. Departure time choice behavior with disposition toward non-active modes, such as car is also examined. Integrated choice latent variable modeling is used as methodological approach. The results of these two models are then compared and discussed in detail. Results indicate that both the models have contrary results to each other with respect to latent preferences for respective modes. Some socio-economic factors came to be more significant than latent preferences. Individuals who prefer active modes such as bicycle have more probability to choose morning departures to evening ones, while the opposite behavior is true for the ones who have latent preferences toward non-active modes. It is also found that car and bicycle mode preferences possess opposing elasticities. With the increase in latent preferences for active travel mode, probability for morning departure increases while opposite is true for latent preference for car.

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

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.

Modal split and commuting patterns on a bottleneck-constrained highway with peak-only bus lane

ABSTRACT In this paper, commuters’ travel mode and departure time choice behaviors on a bottleneck-constrained highway with a peak-only bus lane (PBL) are investigated. The PBL occupies part of the capacity of the bottleneck when it is open. The capacity of the bottleneck for both auto and bus modes is time-varying. As the total number of commuters grows, we consider three regimes with different equilibrium commuting patterns – low, medium and high travel demand. The analytical results for the arrival rates of the two modes are presented and the numerical results are consistent with the analytical results. When the PBL is instituted, the number of bus mode commuters increases and total travel cost decreases with other parameters remaining fixed. The optimal capacity and opening period for the peak-only bus lane and the optimal bus dispatch frequency are discussed in detail.

Travel cost budget based user equilibrium in a bottleneck model with stochastic capacity

This paper studies a bottleneck model in which the capacity of the bottleneck is constant within a day but changes stochastically from day-to-day between a designed value (good condition) and a degraded one (bad condition). The study relates the travel cost variability due to stochastic capacity with commuters' departure time choice behaviors. We postulate that commuters acquire the variability of travel cost based on past experiences and factor such variability into their departure time choice consideration by minimizing their travel cost budget (TCB), defined as a weighted average of mean travel cost and standard deviation of travel cost. We show that the consideration of TCB yields seven possible equilibrium patterns. Closed form solutions to all possible equilibrium patterns and their corresponding parameter ranges are derived. The rationality of the patterns has been investigated. Dependence of travel cost and the duration of peak hours on the commuters' risk attitude has also been derived in each equilibrium pattern. Finally, numerical studies have been conducted to illustrate the properties.

Analysis of the departure time choices of metro passengers during peak hours

In this study, a stated preference (SP) survey based on a D-efficient experimental design was conducted for the Nanjing Metro to examine the passenger flow and elaborate the heterogeneity of the departure time choice behaviour of metro passengers during peak hours. A latent class logit model was applied to calibrate the data, and the attribute variables were studied using an elasticity analysis. The survey identified four classes of metro passengers in peak hours who have different preferences for the departure time choice. The results show that fixed working hours and arrival time constraints are the major attributes that determine the classification of the passengers, while travel costs and overcrowding are the key factors that affect the departure time choice. However, passengers are more sensitive to metro fares than crowdedness. By reducing travel costs and mitigating overcrowding, passengers’ choice of departure time, especially when deciding whether to depart earlier than usual, was significantly affected. This study aims to provide theoretical support and a basis for decision-making for metro operation departments to enact passenger flow control strategies.

Holiday travel behavior analysis and empirical study with Integrated Travel Reservation Information usage

Abstract The efficient travel demand management is more important than traffic congestion control. Currently, tour reservation system (TRS) based on demand response has become a significant part of travel demand management. The Integrated Travel Reservation Information (ITRI) released by TRS plays an important role in the decision-making process of tourist’s holiday travel destination and departure time choice behaviors, which is seldom investigated. To fill this gap, this paper explored the feedback mechanism among the ITRI usage, tourism utility of various alternatives and tourist decision-making behavior based on the Engel-Kollat-Blackwell (EKB) consumer purchase decision-making model. To quantify the impact of ITRI on travel behavior, a revealed preference and stated preference survey were designed and carried out in China. The data show about 80% of tourists are concerned about the information of tickets sold ratio. Nested Logit (NL) model for the joint choice of destination and departure time was established, which accounted for the impact of TRS and ITRI on the utility of alternatives. The NL model results reveal that tourist’s age, education level, number of visits, ITRI content, ITRI query method and the tickets sold ratio have significant effects on destination and departure time choice. Especially, sensitivity analysis results reveal that when the tickets sold ratio reaches 65%, 75%, 85% and 95% respectively, every 1% growth of tickets sold ratio is accompanied by 2.858%, 2.877%, 3.015% and 3.362% increase in the probabilities of traveling during non-holiday periods. Therefore, tourists are encouraged to travel during non-holiday periods by tickets sold information release, which can achieve the rational allocation of tourism resources and enable to make maximum use of tourism resources. Further, the results can provide a basic data for development of the tourism demand management and equilibrium of the tourist’s spatial and temporal distribution.

Will information and incentive affect traveler’s day-to-day departure time decisions?—An empirical study of decision making evolution process

The uncertainty and unpredictability of a transportation network roots in the dynamics of individual travel behavior, which can be revised consciously or repeated habitually depending upon the reality and personality. In this paper, we propose to study the day-to-day departure time choice behavior of the travelers, using real observation data collected from a smartphone app, “Metropia”. Influenced by the information and incentives provided in the app and the comparison with the experience gained from the last trip, a transformation process of traveler’s day-to-day experience on departure time from an existing habit to a new one is analyzed in this study. The analysis result in a binary choice model for the shift of departure time for each repeated morning commute trip comparing with the last one, which proves that users’ experience in app engagement, previous travel time saving, habitual travel time, incentives, and commute flexibility are able to trigger day-to-day behavioral change for their morning home-to work commutes. The findings of this research provide insights on the users’ adaption to a new traffic information service along with incentives, and corresponding behavior changes over a transition period. The outcomes suggest ways to improve ICT services and incentive scheme, as well as the transportation operation and demand management.

Commuter Departure Time Choice Considering Parking Space Shortage and Commuter’s Bounded Rationality

In order to better understand how commuters decide departure time considering parking space shortage and commuters’ bounded rationality, the reference point hypothesis of prospect theory is applied in the departure time decision-making. Commuter personal perception differences, the road congestion situation, destination parking status, and other factors were also analysed in the influence of commuter departure time choice. Based on prospect theory, an experiment was designed to investigate the intention of the commuter departure time choice. The experiment results show that the commuter’s travel satisfaction and the departure time choice of the next trip are related to the parking space residual status after the commuter arrives at the destination. The satisfaction degree of the commuter is reduced, with the decrease of the remaining parking spaces. If the commuter is satisfied with the travel result, the commuter’s departure time of next trip tends to be later. In the case of illegal parking, different penalty measures may lead to different decisions of next departure time choice. A commuter tends to depart earlier when more severe punishment for illegal parking is enforced. The research results can reveal to some degree the travellers’ departure time choice behaviour when they face the risk of no parking spaces and provide a theoretical and practical support for parking management and car travelling decision.

Departure Time Choice (DTC) Behavior for Intercity Travel during a Long-Holiday in Bangkok, Thailand

Time-of-day (TOD) or departure time choice (DTC) has become an interesting issue over two decades. Many researches have intensely focused on time-of-day or departure time choice study, especially workday departures. However, the travel behavior during long-holiday/intercity travel has received relatively little attention in previous studies. This paper shows the characteristics of long-holiday intercity travel patterns based on 2012 New Year data collected in Thailand with a specific focus on departure time choice of car commuters due to traffic congestion occurring during the beginning of festivals. 590 interview data were analyzed to provide more understanding of general characteristics of DTC behavior for intercity travel at the beginning of a Bangkok long-holiday. Moreover, the Multinomial Logit Model (MNL) was used to find the car-based DTC model. The results showed that travelers tend to travel at the peak period when the parameters of personal and household are not so significant, in contrast to the trip-related characteristics and holiday variables that play important roles in traveler decision on departure time choice. Finally, some policies to distribute travel demand and reduce the repeatable traffic congestion at the beginning of festivals are recommended.

Determinants of Travel Choice Behaviour with Travel-Time Variability in the Presence of Real-Time Information

This paper provided a statistical analysis of route and departure time choice behaviour under the effects of travel-time variability and real-time information for commuters in Japan using a web-based stated preference survey data. Furthermore, we developed a model predicting the relationship between the shift in average travel time and its reliability with the average travel time and real-time information provided. A factorial ANOVA underscored a significant effect of travel-time variability and real-time information on both departure time and route choice behaviour. The proposed model estimated the expected travel cost based on the estimated optimal departure time before and after having real-time information. This model may be useful for evaluating the benefit of travel-time reliability improved by the provision of real-time information.

Decision dynamics of departure times: Experiments and modeling

A fundamental problem in traffic science is to understand user-choice behaviors that account for the emergence of complex traffic phenomena. Despite much effort devoted to theoretically exploring departure time choice behaviors, relatively large-scale and systematic experimental tests of theoretical predictions are still lacking. In this paper, we aim to offer a more comprehensive understanding of departure time choice behaviors in terms of a series of laboratory experiments under different traffic conditions and feedback information provided to commuters. In the experiment, the number of recruited players is much larger than the number of choices to better mimic the real scenario, in which a large number of commuters will depart simultaneously in a relatively small time window. Sufficient numbers of rounds are conducted to ensure the convergence of collective behavior. Experimental results demonstrate that collective behavior is close to the user equilibrium, regardless of different scales and traffic conditions. Moreover, the amount of feedback information has a negligible influence on collective behavior but has a relatively stronger effect on individual choice behaviors. Reinforcement learning and Fermi learning models are built to reproduce the experimental results and uncover the underlying mechanism. Simulation results are in good agreement with the experimentally observed collective behaviors.

Integrating the mean–variance and scheduling approaches to allow for schedule delay and trip time variability under uncertainty

Uncertainty of travel times and the impact on travel choice behavior has been recognized as an increasingly important research direction in the past decade. This paper proposes an extension to the popular scheduling approach to model traveler’s departure time choice behavior under uncertainty, with the main focus on a richer representation of uncertainty. This more general approach incorporates a separate term to reflect the risk aversion associated with uncertainty. Recognizing the correlation between expected schedule delay and travel time variability, the schedule delay components in the generalized approach are defined in terms of expected travel time, which differs from the scheduling approach. This approach is developed based on the analytical investigation of the relationship between the expected schedule delay and the mean and standard deviation of travel time. An analytical equivalence was found between the scheduling approach and the general approach given a departure time t. To investigate the empirical performance of the generalized approach, two state preference (SP) data sets are used; one from China with a symmetric travel time distribution and the other from Australia with an asymmetric distribution. Both studies show empirical evidence of an equivalence in respect of statistical fit between the generalized and the scheduling approaches, as found from analytical investigations. The Chinese study gives support in the generalized model to including both the mean–variance and the scheduling effects; whereas the Australian study finds only the mean–variance specification has statistical merit. Despite the different travel contexts, it is noteworthy in both empirical settings, that the parameter estimate for arriving earlier than the preferred arrival time (PAT) in the generalized model is positive. This suggests that commuters tend to prefer to arrive earlier in order to guarantee he/she will not be late. This paper contributes to a better understanding of performances of different reliability measures and their relationships. The practical value of the various unreliability measures is provided showing that these indicators are easy to obtain for inclusion in project appraisal.

Departure time choice behavior for hurricane evacuation planning: The case of the understudied medically fragile population

Despite the non-trivial number of (non-institutionalized) residents with special needs – referred to as the medically fragile population in this paper – there is virtually no research available to guide the hurricane evacuation planning for this population group. One area is their evacuation time choice behavior that can have tremendous implications for their safety as well as the logistics of the evacuation process. In this paper, we fundamentally advance our understanding of the evacuation timing behavior of this heavily understudied, and potentially vulnerable, population group. Analysis indicates that key differences exist between the medically fragile and non-medically fragile population.

Stochastic Bottleneck Model with Heterogeneous Travelers

This paper studies commuters' departure time choice behavior along a bottleneck constrained highway connecting a resident area and a workplace. Commuters are differentiated by their value of time, and the bottleneck capacity is assumed to follow a uniform distribution within a range. The equilibrium properties of the proposed model are derived and individual travel cost is obtained. It is found that at equilibrium, the increase of capacity variability would change the commuters' travel behavior by increasing the expected equivalent travel time and lengthening the peak period. Every commuter is better off by reducing the individual travel cost if the stochasticity of the capacity decrease, and commuters with high value of time benefit more than those with low values. Numerical results are presented to support analytic findings.