Changing Departure Times Research Articles

Research on Time-Based Fare Discount Strategy for Urban Rail Transit Peak Congestion

To alleviate peak-hour congestion in urban rail transit, this study proposes a new off-peak fare discount strategy to incentivize passengers to shift their departure time from peak to off-peak hours. Firstly, a questionnaire survey of Shanghai metro passengers is conducted to analyze their willingness to change departure time under different fare strategies. Secondly, based on the survey results, a time-differentiated fare discount model is constructed, considering both the company’s revenue and passengers’ travel benefits, and with the optimization objective of achieving balanced peak-hour and off-peak-hour train loads throughout the day. Subsequently, a genetic algorithm with nested fmincon functions is designed and combined with the actual data of Shanghai rail transit line 9 for arithmetic analysis. Finally, the effectiveness of the model is validated using the survey data. The research results show that the off-peak fare discount strategy can incentivize 6.88% of passengers traveling in the morning peak and 6.66% of passengers traveling in the evening peak to shift to off-peak travel. This research provides theoretical support and decision-making guidance for implementing time-differentiated pricing in urban rail transit systems.

Incident Diversionary Routing: Survey of Influences and Decisions

When confronted with congestion and delay, drivers often divert their route. Depending on factors like purpose, urgency, destination, route alternatives, type of disruption, and mode options, common diversionary options can range from changing departure time, route, and mode to canceling a trip altogether. The choice to divert depends on several factors, including whether, when, and where they become aware of a disruption. As the ability of transportation agencies to detect incidents, inform drivers of conditions, and implement control-changes to accommodate volume surges has improved, there is increasing interest in knowing whether drivers will divert and why. This article describes the results of research using marketing-based survey techniques to evaluate driver diversionary behavior during roadway incidents. The survey was used to identify and assess diversionary choice-making a) based on travel behaviors and habits, b) under routine and adverse conditions, c) for different incidents and route options; and d) under varied guidance information available to them. The results showed that travel time reduction was the primary motivator for alternate path use to avoid congestion, consistent with previous research. Interestingly, younger males showed the lowest level of influence from guidance information, and route familiarity had a lower influence on diversionary routing among all groups, suggesting a trust in real-time mobile routing guidance. With additional analyses, these findings could be applied to estimate and model driver actions under a range of driver-, network-, travel-, and disruption conditions.

Measures reducing travel by public transport during peak hours

Generally, the cost of public transport has increased. Previous research has identified peak hour traffic as the main cost driver, mostly because the travel demand increases the number of vehicles and drivers needed. The purpose of this study is to identify measures aiming to reduce travel with public transport during peak hours, conducted through a semi-systematic literature review. The measures found were fare price differentiation, rewards, information regarding vehicle crowding, staggered school hours, flexible working hours, and working remotely. No studies of soft measures (e.g., campaigns and travel advice) for transport demand management were found. The results indicate that commuters with flexible working hours might have limiting factors that affect their ability to change departure time, for example, activities in connection to work and family responsibilities.

Identifying Crowding Impact on Departure Time Choice of Commuters in Urban Rail Transit

Crowding in urban rail transit is an inevitable issue for most of the high-density cities across the world, especially during peak time. For commuters who have considerably fixed destination arrival times, departure time choice is an important tool to adjust their trips. The ignorance of crowding impact on commuters’ departure time choice in urban rail transit may cause errors in forecasting dynamic passenger flow during peak time in urban rail transit. The paper develops a mixed logit model to identify how crowding impacts the departure time choice of commuters and their taste variation. Arrival time value was firstly measured in a submodel by applying the reference point approach and then integrated to the main model. Considering the characteristics of human perception, we divided crowding into five grades with distinct circumstances. All parameter distributions were assumed based on their empirical distributions revealed through resampling. The data from Shanghai Metro used for estimation were collected by a specifically designed survey, which combines revealed preference questions and stated preference experiments to investigate the willingness and extent of changing departure time choice of passengers who experienced various grades and duration of crowding in the most crowded part. The result shows that an asymmetric valuation model with preferred arrival time as the only reference point best captured commuters’ responses to arrival time. The departure time choice model clearly identified that only crowding ranging from Grades 3 to 5 had an impact on commuters’ departure time choice. The parameters of crowding costs can be assumed to follow transformed lognormal distributions. It is found that the higher the grade of crowding is, the bigger the impact each unit of crowding cost has on commuters’ departure time choice, while commuters’ tastes get more concentrated when crowded situation upgrades. The model in this paper can help policymakers better understand the interaction between commuters’ departure time choice and crowding alleviation.

Modelling preferences towards activities and their effect on departure time choices

A great challenge that comes with cities’ growth and urbanization is how to transport their inhabitants efficiently. Since available urban space has been progressively reducing, the attention has shifted from capacity investment to transport demand management. As a result, policies aimed at changing departure time choices have arisen. To properly analyze such policies, it is necessary to understand how and when people prefer to travel, which is a result of the activities individuals perform. This paper studies preferences towards activities to be conducted and the impact of these preferences on departure time choices. For this purpose, a stated preference survey was developed to reproduce a daily activity pattern for all respondents. With the collected data it was possible to compare alternative approaches to model activities proposed in the literature. This comparison shows that traditional formulations to model activities preferences might not be the ideal in some cases. Instead, the use of more complex formulations, such as sigmoidal functions, is encouraged since they could adequately represent the preferences regarding activity's duration and time of day, as well as activity's flexibility. Results show that activities to be conducted before and after each trip affect the departure time choices and should be considered when modelling and forecasting travel demand. Therefore, a proper benefit estimation from travel time savings should include a direct and indirect component: one related to avoid travelling (which is an undesired activity by itself) and another one related to the increase of available time to perform activities instead of travelling. Our results show that if the latter effect is not included, the value of mean travel time would be underestimated by 38% and total benefits could be miscalculated.

Measuring Retiming Responses of Passengers to a Prepeak Discount Fare by Tracing Smart Card Data: A Practical Experiment in the Beijing Subway

Understanding passengers’ responses to fare changes is the basis to design reasonable price policies. This work aims to explore retiming responses of travelers changing departure times due to a prepeak discount pricing strategy in the Beijing subway in China, using smart card records from an automatic fare collection (AFC) system. First, a new set of classification indicators is established to segment passengers through a two-step clustering approach. Then, the potentially influenced passengers for the fare policy are identified, and the shifted passengers who changed their departure time are detected by tracing changes in passengers’ expected departure times before and after the policy. Lastly, the fare elasticity of departure time is defined to measure the retiming responses of passengers. Two scenarios are studied of one month (short term) and six months (middle term) after the policy. The retiming elasticity of different passenger groups, retiming elasticity over time, and retiming elasticity functions of shifted time are measured. The results show that there are considerable differences in the retiming elasticities of different passenger groups; low-frequency passengers are more sensitive to discount fares than high-frequency passengers. The retiming elasticity decreases greatly with increasing shifted time, and 30 minutes is almost the maximum acceptable shifted time for passengers. Moreover, the retiming elasticity of passengers in the middle term is approximately twice that in the short term. Applications of fare optimization are also executed, and the results suggest that optimizing the valid time window of the discount fares is a feasible way to improve the congestion relief effect of the policy, while policy makers should be cautious to change fare structures and increase discounts.

Assessment of Commuter Train Timetables Including Transfers

Abstract Many railway networks suffer from high capacity utilisation. For scheduling all services, adjustments to the desired slots are often needed. Such adjustments might lead to longer travel times, crowded trains, longer waiting times for boarding and for transfers. All of this has an important socio-economic impact on both travellers and train operators. This raises the question of the socio-economic assessment of changes in commuter train timetables including transfers. Thus, the aim of this study is to evaluate the effects of adjustments of commuter train timetables on the traveller (i.e. consumer costs) and the train operator (i.e. producer costs). These costs are estimated based on all train trips and operations in the network. In a case study, the effect of changes in departure times (resulting in non-regular interval timetables) is analysed. Further, the price of cancelling a two-way service during different times of the day is compared. The results show that changing departure times can both decrease and increase the total costs, and that regularity for parallel services might not be as important as expected if it is kept for each separate service. For the second study, waiting times for transfers were indicated to have a (too) large impact which can lead to misleading results and might be adjusted in future work. The model is adequate for such kind of questions but needs some more adjustments. For railway networks with dense and heterogeneous traffic (as is the case in Sweden), the contributions of this model are useful for making the challenging timetabling process easier and commuter train services less costly.

Commuters’ attitudes and norms related to travel time and punctuality: A psychographic segmentation to reduce congestion

Congestion remains one of the most prevalent transport problems in big cities. As a starting point for more targeted interventions to reduce congestion, this paper suggests a segmentation of commuters. Based on psychographic factors derived from an expanded Theory of Planned Behaviour, we identify three distinct commuter segments: (1) Unhurried timely commuters, who find it very important to arrive on time but less important to have a short travel time; (2) Self-determined commuters, who find it less important to arrive on lime and depend less on others for their transport choices; and (3) Busy commuters, who find it both important to arrive on time and to have a short travel time. Comparing the segments based on background variables shows that Self-determined commuters are younger and work more often on flextime, while Unhurried timely commuters have longer distances to work and commute more often by public transport. Results of a discrete departure time choice model, estimated based on data from a stated preference experiment, confirm the criterion validity of the segmentation. A scenario simulating a toll ring illustrates that mainly Self-determined commuters would change their departure time as a response to this economic intervention, while we suggest alternative interventions for the two other segments. The results stress the need for more targeted efforts to change departure time choice and point to ways to improve the suggested segmentation approach.

An impact analysis of traffic image information system on driver travel choice

A driver is one of the main components in a transportation system that influences the effectiveness of any active demand management (ADM) strategies. As such, the understanding on driver behavior and their travel choice is crucial to ensure the successful implementation of ADM strategies in alleviating traffic congestion, especially in city centres. This study aims to investigate the impact of traffic information dissemination via traffic images on driver travel choice and decision. A relationship of driver travel choice with respect to their perceived congestion level is developed by an integrated framework of genetic algorithm–fuzzy logic, being a new attempt in driver behavior modeling. Results show that drivers consider changing their travel choice when the perceived congestion level is medium, in which changing departure time and diverting to alternative roads are two popular choices. If traffic congestion escalates further, drivers are likely to cancel their trip. Shifting to public transport system is the least likely choice for drivers in an auto-dependent city. These findings are important and useful to engineers as they are required to fully understand driver (user) sensitivity to traffic conditions so that relevant active travel demand management strategies could be implemented successfully. In addition, engineers could use the relationships established in this study to predict drivers’ response under various traffic conditions when carrying out modeling and impact studies.

A Bi-level Formulation for the Combined Dynamic Equilibrium based Traffic Signal Control

This paper formulates the combined dynamic user equilibrium and signal control problem (DUESC) as a bi-level optimization problem. The signal control operator in the upper level optimizes the signal setting to minimize the system travel time whereas the road users in the lower level minimize their own costs (by changing departure times, paths or both) leading to dynamic user equilibrium behavior. Three components of the bi-level formulation are discussed including network loading model, the dynamic user equilibrium model and the signal control model. Then the combined problems are formulated as a Nash-Cournot game and a Stackelberg game. A solution technique based on the iterative optimization and assignment (IOA) method is proposed to solve the DUESC problem. We use the projection algorithm to solve the lower level and the mixed integer programming solver to solve the upper level. Extensive numerical results demonstrate the benefits of using this model.

Impacts of time-varying cordon pricing: Validation and application of mesoscopic model for Stockholm

This paper uses a simulation model to compare traffic and welfare effects of changes to the charging schedule currently in use in Stockholm. In particular, a step toll is compared to its flat counterpart at two charging levels. The increments between steps are also increased in a peaked step toll scenario. Furthermore, results from simulation of the current toll ring are compared to real-world measurements in a first attempt to validate model predictions regarding impacts of a time-varying congestion charging scheme. In the model, car users have the possibility to respond to congestion charging by changing departure time, route or switch to public transport and travel times are calculated using mesoscopic traffic simulation. Validation shows that departure time choice adjustments because of congestion charging are overestimated by the model that is based on stated preference data. This warrants further research on discrepancies between stated and revealed adjustments to congestion charging.The current step toll reaches the highest social benefit estimate in model predictions, but differences in traffic effects between the current step toll and its flat counterpart are rather small. Furthermore, results show that demand changes occur in the model to a considerably greater extent for trips with low value of time. The differences in welfare effects is for that reason large for different trip purposes, indicating the importance of accounting for heterogeneous trips when modelling effects of congestion charges.

Analysis of Awareness and Use of Traffic Information System with Revealed Preference Data

To alleviate traffic congestion on Malaysia's highways and roads, the Integrated Transport Information System (ITIS) was established in Klang Valley, Malaysia, to collect and disseminate updated traffic information. One important aspect of this system is the use of various traffic information tools [Internet, radio, and variable message signs (VMS)] in disseminating traffic information pretrip and en route. To investigate the effectiveness of ITIS, this paper assesses the level of awareness and use of ITIS in the Klang Valley region. Using a revealed preference survey and adopting a discrete choice modeling approach, researchers found that the level of awareness (58% for VMS, 22% for radio pretrip, 48% for radio en route, and 12% for Internet) and use (29% change departure time and 22% divert) of ITIS were low in the region. Driver demographics had little impact on the awareness and use of ITIS, whereas trip characteristics played an important role. Drivers in the Klang Valley region had limited trust in the accuracy of data provided by ITIS and did not perceive such data to be useful in reducing their travel time. These findings demonstrate the need for strategies to improve awareness of ITIS and to increase its credibility.

Estimation of the Value of Winter Road Information on Mountain Passes

Winter Mountain Pass Guide Experiment has been carried out to improve quality of mobility by providing road information of mountain passes to road users. The effect of winter road information provision on mountain pass is to change drivers' transportation behavior choices, such as changing departure time, canceling the trip and changing the travel route. However, it is a problem if road administrator considers to the future employment as the road project that the framework of cost-benefit evaluation for such a road information provision has not established yet. In this study, a stated preference survey was carried out to the monitors of the Winter Mountain Pass Guide to estimate the value of road information provision for measuring the appropriate scale of the project, and the benefit of the value of road information was experimentally calculated by using the exist evaluation model.

Analyzing work departure time variability in Brussels

– This study explores the dynamics of departure time for the afternoon commute, which has received little attention both in theoretical and empirical work. Reported monthly and weekly departure time data, obtained from a survey of Brussels commuters, are used to study the impact of socio-economic, transportation and workrelated variables on the propensity of afternoon departure time changes. Binary and ordered probit models are used to estimate the impacts of these factors on two measures of departure time change propensity. The first one is the reported frequency of departure time changes in a month and the second measure is the time (in minutes) that a respondent left earlier or later than their normal time during the last five working days. In the first measure, the respondent selects his or her own threshold for reporting departure time change, whereas in the second measure the respondent merely provides the deviation from his or her normal departure time. The two measures together suggest three types of respondent groups: A group that perceives a wide-window (greater than or equal to 30 minutes) of regular departure times as normal, a group that perceives a narrow window of departure time as normal and change departure time occasionally and a third group that does not change their normal departure time. Statistical evidence indicates that work-related factors, particularly tolerant policy of the employer toward leaving work earlier, flextime and occupation type (scientific and executive professions) are associated with higher propensity of departure time changes. The implications of the findings are discussed.

Methodology for Assessing High-Occupancy Toll-Lane Usage and Network Performance

A modeling framework for the evaluation of high-occupancy toll (HOT) facilities is discussed. It is based on a traffic simulation-assignment model interfaced with an unordered multinomial logit mode-choice model. The model allows for the prediction of high-occupancy, transit, and single-occupancy vehicle modal shares, based on prevailing traffic conditions and tolls. Also presented is an illustrative investigation of the sensitivity to various elements of the modal-choice function using the methodology and of the effect of different HOT facility features on HOT lane usage and associated system performance. These features include HOT lane configurations, pricing levels, carpooling attractiveness, changing departure times, and sensitivity to generalized cost. A representation of the south-central part of I-35W and the supporting roadways in the Fort Worth, Texas, area was used in the study. The results of the simulations were analyzed under each of the operating and behavior characteristics to evaluate the potential success of an HOT facility. Improving the carpooling attractiveness decreases the number of vehicles on the roadway but may not necessarily improve the average travel time for the system as a whole. The larger the departure-time window, the easier it is for travelers to carpool; however, the relatively small number of people choosing the HOV mode may inhibit the average travel time for the system from reflecting the impacts of the reduced vehicle trips. Increasing the sensitivity to time, or generalized cost, raises the HOV mode share when there is an HOT facility available.

Commuters’ Departure Time Decisions in Brussels, Belgium

Factors that influence commuters’ departure time decisions are explored, especially the trade-off between travel time and schedule delay. Stated and reported behavior data obtained from a survey of commuters in Brussels, Belgium, were used to analyze the influence of socioeconomic and contextual variables. The key findings were as follows. Daily schedules for flextime and fixed-time commuters were quite similar, suggesting that flextime commuters do not extensively use their flexibility to avoid peak-period congestion. When commuters changed their departure times between home and work, their arrival times shifted by a similar amount. This implies that the shortening of travel time is not as critical as other reasons, such as requirements and personal convenience, in motivating departure time changes. Furthermore, 35 to 50 percent of the respondents were unwilling to change their departure times to save 10 min of travel time. Therefore, departure time changes may not be feasible in many cases for the range of travel times encountered in urban areas. Among those willing to make further trade-offs by changing departure times, the values for the early and late schedule delay–travel time trade-off were similar for both the stated and the reported preferences and were broadly consistent with those from other studies. The travel time–schedule delay trade-off values are calculated for the a.m. and p.m. commutes. Commuters who experienced longer travel times were more likely to change their departure times. When changing departure times, females and managers were less likely to depart from home later than usual, and managers were also more likely to depart earlier than usual. To analyze relationships empirically, ordinary-least-squares and tobit models of departure time are estimated. Finally, the implications are discussed.

The effect of motorist information on commuter behavior: Classification of drivers into commuter groups

This paper describes a methodology originally devised for analysis of travel-activity patterns and applies it to commuters' responses to the influence of traffic information upon commuting decisions. The method of cluster analysis was employed to identity commuter groups (from 3,893 motorists who responded to an on-road survey) with similar patterns of responses to the influence of traffic information. The resulting groups were defined as (a) route changers, willing to change route both on Interstate 5 and before leaving; (b) non-changers, unwilling to change departure time, route, or mode of transportation; (c) route and time changers, willing to change route and departure time; and (d) pre-trip changers, willing to change departure time, route, or mode before departure but unwilling to change en route. Knowledge of such groups and their behavioral characteristics is useful in designing advanced traveler information systems that seek to affect commuter behavior and increase the efficiency of current transportation facilities. This paper discusses the methodology used to derive the commuter groups and investigates their commuting behaviors, decision-making processes, and information needs.