Departure Time Choice Research Articles

Trial-and-Error Congestion Pricing Scheme for Morning Commute Problem with Day-to-Day Dynamics

A trial-and-error congestion pricing scheme finds the optimal toll based only on observable information (e.g., travel time) without information on travelers’ personal preferences that are often unobservable in practice (e.g., value of time, scheduling preference). This paper proposes a trial-and-error pricing scheme for the morning commute problem, also known as the departure time choice problem and Vickrey’s bottleneck model, with day-to-day dynamics. The proposed scheme is guaranteed to find the social optimal congestion toll under somewhat restrictive assumptions. We numerically confirm that the proposed scheme converges to the social optimal toll regardless of the parameter setting, and it decreased the social cost fairly quickly.

Managing rail transit peak-hour congestion with step fare schemes

In this paper, we propose step fare schemes for a rail transit line connecting a residential district with a central business district (CBD). The proposed fare schemes aim to reduce the total system cost (TSC) while ensuring revenue-neutrality for the rail operator. The user equilibrium (UE) principle in terms of generalized trip cost is adopted to formulate commuters’ departure time choice problems with late arrival under the proposed fare schemes. The equilibrium flow patterns and trip costs are analytically derived. It is found that passengers’ departure times present more even distribution under the schemes with more fare steps, resulting in a lower TSC. Besides, the optimal train schedule and the optimal number and capacity of trains are analyzed. Finally, numerical examples are provided to illustrate the performance of the proposed transit fare schemes.

Flow pattern and optimal capacity in a bi-modal traffic corridor with heterogeneous users

This paper considers a bi-modal traffic corridor with a highway and a transit line where car-available-travelers tend to be more sensitive to crowding than car-unavailable-travelers in transit vehicles. Modal spilt and departure time choices of heterogeneous travelers (car-available- and car-unavailable-travelers) are considered. Two capacity design problems, namely total travel cost minimization with budget constraint and total system cost minimization with budget and equity constraints, are studied. It was found that when there is a construction/operational technology improvement for one traffic mode, the cost for its unit capacity reduces and its optimal total investment may increase (i.e. Jevons paradox).

Departure and travel time model for the temporal distribution of morning rush-hour traffic congestion

The chronic traffic congestion undermines the level of satisfaction within a society. This study proposes a departure time model for estimating the temporal distribution of morning rush-hour traffic congestion over urban road networks. The departure time model is developed based on the point queue model that is used for estimating travel time. First, we prove the effectiveness of the travel time model (i.e. point queue), showing that it gives the same travel time estimation as the kinematic wave model does for a road with successive bottlenecks. Then, a variant of the bottleneck model is developed accordingly, aiming to capture travelers’ departure time choice for commute trips. The proposed departure time model relaxes a traditional assumption that the last commuter experiences the free flow travel time and considers travelers’ unwillingness of late arrivals for work. Numerical experiments show that the morning rush-hour generally starts at 7:29 am and ends at 8:46 am with a traffic congestion delay index (TCDI) of 2.164 for Beijing, China. Furthermore, the estimation of rush-hour start and end time is insensitive to most model parameters including the proportion of travelers who tend to arrive at work earlier than their schedules.

Simulation-based joint optimization framework for congestion mitigation in multimodal urban network: a macroscopic approach

Travel demand management (TDM) is an important measure that will aid in the realization of efficient and sustainable transportation systems. However, in cities where the most serious traffic congestion occurs, implementation of a single TDM measure might not be enough to reduce congestion, because the congestion mechanism in this case is highly complex and involves different transportation modes interacting with each other. Implementation of multiple TDM measures has rarely been discussed in the literature. Therefore, in this study, we propose a simulation-based joint optimization framework composed of dedicated bus lanes and vehicular congestion pricing. The objective of the optimization process is to minimize the congestion cost based on an advanced macroscopic flow theory called the multimodal macroscopic fundamental diagram (mMFD), which can capture the macroscopic traffic dynamics of multimodal transportation systems. In the proposed framework, we develop mMFD-based congestion pricing scheme and incorporate traveler’s behavioral model (i.e. joint departure time and mode choices) with the microscopic traffic simulator. We consider the Tokyo central area as a case study. The simulation results indicate that space allocation of 4.7% for the dedicated bus lanes would be optimal for Tokyo’s network, while the optimal congestion pricing scheme indicates that charges of 900 JPY between 7:30 and 8:00 AM and 300 JPY between 8.00 and 8:30 AM should be levied.

Mode shift impacts of optimal time-dependent congestion pricing in large networks: A simulation-based case study in the greater toronto area

This paper presents a case study on commuter’s departure time, route and mode choice responses to optimized tolling in the Toronto, Ontario, Canada. The study integrates a toll optimization component into an integrated framework of econometric departure time choice, dynamic traffic assignment model, and random utility maximization (RUM) based mode choice model. An iterative optimization algorithm is used to generate optimal tolling structure which explicitly considers heterogeneous user preferences through econometric departure time choice modelling component. The mode choice component is exogenously retrofitted in an integrated econometric departure time choice modelling component and dynamic operational traffic assignment model to capture individual mode choice behaviour in response to variable congestion pricing. The integrated model results show that individuals are more likely to switch driving routes and departure times than commuting modes when making tradeoffs between schedule delay cost, travel time cost, and toll cost. It is found that the optimal toll scenario improves the network-wide travel time. The modelling framework presented in this study can test a wide range of policy scenarios including the time-variable tolling of congested highways, and high occupancy toll lanes. While the integrated framework presented here is tested at a large scale on the Greater Toronto Area (GTA), we believe the approach can be applied to large-scale networks globally where the objective is seeking the best network-wide spatial and temporal traffic distribution.

A dynamic user equilibrium model for multi-region macroscopic fundamental diagram systems with time-varying delays

Macroscopic fundamental diagram (MFD) has been widely used for aggregate modeling of urban traffic network dynamics to tackle the dimensionality problem of microscopic approaches. This paper contributes to the state-of-the-art by proposing a dynamic user equilibrium (DUE) model that enables simultaneous route choice and departure time choice under the MFD framework for various applications such as park-and-ride, vehicle dispatching and relocation. To better capture the traffic flow propagation and to adapt to the fast time-varying demand, the state-dependent travel time function is integrated into the MFD dynamics as an endogenous time-varying delay. The multi-region MFD dynamics with saturated state and inflow constraints is then used as the network loading model to formulate the DUE model through the lens of the differential variational inequality. Necessary conditions for the DUE are analytically derived using the Pontryagin minimum principle. Difficulties raised in handling the dynamic state-dependent nonlinear travel time functions, state and inflow constraints are addressed without model linearization nor enforcing constant delay assumption as conventionally done in the literature. The additional cost induced by inflow capacity and accumulation constraints can capture the hypercongestion represented by the downward sloping part of the MFD without actually activating traffic congestion. Numerical examples solved by using time-discretization solution algorithm illustrate the DUE characteristics and the corresponding dynamic external costs induced by constraints.

Effects from usage of pre-trip information and passenger scheduling strategies on waiting times in public transport: an empirical survey based on a dedicated smartphone application

Waiting times are important indicators of the degree of travel time optimisation and other behavioural traits among public transport (PT) passengers. As previous studies have shown, the level and usage of pre-trip information regarding schedule or real-time departures are important factors that influence the potential to realise travel time savings by enabling PT passengers to optimise waiting times. Most empirical evidence regarding the revealed PT travel behaviour concerning information levels is based on manual interviews or traditional travel surveys, in which there is a risk that the actual context of where and when the choice of departure time was made is not taken into account. This paper reports the results of a travel survey based on a dedicated smartphone application applied in a field study in a Swedish mid-size urban and regional context. Context-aware notification prompting was used to allow respondents to state their use of pre-trip information as well as whether they had pre-planned their trip and how contingent planning aids were used for time optimisation. The implications on passenger waiting times of the use of information regarding departure times by passengers were emphasised during analyses of the resulting data, along with personal characteristics, in which auxiliary sources such as timetable data and Automatic Vehicle Location were utilised to determine ground truth trip trajectories and trip-contextual factors. The results indicate the significance of having access to pre-trip information, especially for long trips above one hour’s duration, in order to pre-plan and thereby optimise waiting times. In addition, the use and source of pre-trip information differ among age and gender groups. Trip purpose and time of day to some extent determine waiting times and choice of trip optimisation strategy (arrival or departure time).

Space-time dynamics: A modeling approach for commuting departure time on linked datasets

Commuters' departure time related decisions are important in time geography. Analytic tools have been proposed to capture the inherent choice determinants both in time and space. Although the dynamic aspects of the problem have been identified, most of the existing studies are based on static models. In this paper, a dynamic modeling framework is proposed to explore the relationship between commuters' departure time choices and the evolution of en route traffic. A data linkage method is developed to create an integrated dataset that enables the observation of commuters' reaction to changes in travel time and traffic conditions over time. A regional household travel survey is linked to travel information obtained from the Google Maps application program interface (API), creating a synthetic longitudinal dataset. Two decision rules are applied to model commuters' response to the evolution of traffic. The results indicate that travel time, distance to work location, flexibility in working schedule, expected arrival time, and commuters' sociodemographic influence departure time choices. It is also found that accounting for dynamics improves model fit and out-of-sample predictions. Both the dynamic model and the proposed data linkage method contribute to the understanding of human activities in space and time and can be used to enhance transportation demand analysis and urban policy studies.

Day-to-day evolution of departure time choice in stochastic capacity bottleneck models with bounded rationality and various information perceptions

This study investigates the dynamical evolution of travelers’ departure time choice in a bottleneck-constrained roadway system with stochastic capacity. When considering day-to-day traffic evolution, this study explores two scenarios: travelers have long-term historical knowledge and real-time information provision; and travelers have short-term travel experience and real-time information provision. In this context, this study models boundedly rational travelers. This study sheds light on the trade-off between daily (short-term) equilibrium and statistical (long-term) equilibrium.

Modelling departure time choice using mobile phone data

The rapid growth in passive mobility tracking technologies has led to departure time choice studies based on GPS data in recent years (e.g. Peer et al., 2013). GPS data however typically has limited sample sizes and is affected by technical issues like signal losses and battery depletion leading to gaps in the data. On the other hand, the rapid growth in mobile phone penetration rates has led to the emergence of alternative passive mobility datasets such as Global System for Mobile communication (GSM) data. GSM data covers much wider proportions of the population and can be used to infer departure time information. This motivates this research where we investigate the potential use of GSM data for modelling departure time choice. We describe practical approaches to extract relevant information from GSM data and propose a modelling framework that accounts for the fact that the desired departure times are unobserved. We assume that the preferred departure times vary randomly across the users and apply the mixed logit framework to jointly estimate the distribution parameters of the preferred departure times and the sensitivities to schedule delay. Comparison of the model results and time valuation metrics derived from the GSM data with similar metrics derived from the GPS data of a subset of the users reveals that the fewer time gaps in the GSM data lead to reliable model outputs. The proposed framework can be used for mobile phone and other passive data sources with unobserved preferred departure times.

Application of Discrete 3-level Nested Logit Model in Travel Demand Forecasting as an Alternative to Traditional 4-Step Model

This paper aims to introduce a new modelling approach that represents departure time, destination and travel mode choice under a unified framework. Through it, it is possible to overcome shortages of the traditional 4-step model associated with the lack of introducing actual travellers’ behaviours. This objective can be achieved through adopting discrete 3-level Nested Logit model that represents different potential correlation (cross elasticity) among departure time, destination and travel mode alternatives. The proposed model has been estimated and tested by using discretionary trips’ data from Eskisehir city, Turkey. In the light of the estimation results, individuals tend to jointly decide on discretionary travel dimensions rather than separately as assumed by the traditional 4-step model. Moreover, the proposed approach shows more flexibility in considering attributes of alternatives along with characteristics of decision makers. That results in a more behavioural travel demand modelling, more accurate future forecasting and more trusted policy implications. The proposed model represents a more accurate and reliable alternative for the first 3-steps of the traditional 4-step model in small-scale planning issues. Finally, the proposed approach is a significant milestone toward obtaining a consistent, efficient and integrated full-scale behavioural-model that consists of all travel demand dimensions.

Modelling and managing the integrated morning-evening commuting and parking patterns under the fully autonomous vehicle environment

This study is the first in the literature to analytically investigate the traffic dynamics of the integrated morning and evening commutes when daily trips are completed with autonomous vehicles (AVs). Given the parking locations of AVs resulting from the morning commute, firstly we analyse the evening commuting pattern, at which no traveller can reduce the individual travel cost given other AVs’ times of departures from the parking spaces. The equilibrium traffic pattern at the evening commute is then integrated with the morning commute, where equilibrium choices of departure time from home and parking location are derived and analysed. We then study the integrated morning-evening commuting pattern at the system optimum and develop the road tolling scheme to achieve the system optimum. Furthermore, this study analyses the optimal AV parking supply strategy to minimise the total system cost, which is comprised of the total social parking cost and the total daily travel cost under either user equilibrium or system optimum traffic pattern. We also illustrate the modelling insights through numerical studies regarding relationship among traffic efficiency, tolling schemes and AV parking supply plans. This study highlights the differences in daily commuting and parking patterns between the AV situation and the non-AV situation, and sheds light on how traffic and parking should be managed or planned in the future.

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.

System optimum dynamic traffic assignment with departure time choice on two-terminal networks

ABSTRACTThis paper addresses the system optimum dynamic traffic assignment (SO-DTA) problem with departure time choice on a two-terminal network, where the travel cost consists of travel time and an early schedule delay cost. Under a certain condition, we show that SO-DTA reduces to the latest departure earliest arrival flow (LDEAF), and hence methodologies of LDEAF can be used to study SO-DTA. A successive shortest path (SSP) algorithm is used to solve the LDEAF problem. The benefit is that the SSP algorithm involves only the shortest path computations on a static network. System marginal costs, externalities and dynamic user equilibrium tolls are analyzed in the LDEAF context, and used to provide a better understanding of the SO-DTA problem characteristics. Further, the study findings can be used for the morning commute problem as it is a special case of the SO-DTA problem on a two-terminal network.

Personalized incentives for promoting sustainable travel behaviors

We develop a personalized system to modify individual travel behaviors by offering personalized incentives. Individual preferences are learned to provide personalized incentives so that the promoted alternative is likely accepted. Using knowledge from control theories and state estimation, we model travelers’ choice-making behaviors with the random utility theory and responses from the individuals are mined by a particle filter for learning individual preferences to promote sustainable behaviors. The discrete nature of travel behavior naturally leads to limited observability. We overcome this problem by designing a measurement function from which additional information can be solicited. Additionally, the inherent trade-offs between factors that affect travel choices result in an infinite set of solutions. We thus propose two solutions: (1) the divide and conquer strategy in which a multi-dimensional conditional probability function is proposed; and (2) use of domain knowledge to restrict that preference values fall in certain ranges and are consistent with certain distributions. The performance of preference learning with these two solutions applied is shown via simulation tests and an online experiment involving human participants. For departure time choices, we show an average acceptance ratio of 0.68 for all participants when being promoted with alternatives with personalized incentives. We also show that changes in individual departure time choices will lead to 48% reduction in total travel time on a simple transportation network.

A Pareto-improving and revenue-neutral scheme to manage mass transit congestion with heterogeneous commuters

A hybrid fare scheme (HFS) is proposed in this paper that combines a fare-reward scheme (H-FRS) and a non-rewarding uniform fare scheme (H-UFS) by considering the heterogeneity in transit commuters’ scheduling flexibility. It aims at reducing peak-hour congestion in the urban transit system with alternative options catering for various commuters. In the H-FRS, a commuter will be rewarded with a free ride during the periods preceding or following a given shoulder period after taking a certain number of paid rides during the central period in peak hours. In the H-UFS, a commuter needs to pay a different but uniform fare during peak hours. Commuters will have the opportunity to join either of the sub-schemes according to their scheduling flexibility of departure time choice. The hybrid fare scheme determines the free fare intervals, the rewarding ratio, and the new fares for the sub-schemes. Our results demonstrate that the proposed HFS is not only revenue-preserving but also Pareto-improving. Depending on the original fare, an optimally designed hybrid fare scheme can achieve a reduction in total time costs by at least 25% with the optimal free fare interval.

ARRIVAL TIME VALUATION OF COMMUTERS IN URBAN RAIL TRANSIT

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.

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.