Departure Time Choice Research Articles

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.

Modeling and optimizing a fare incentive strategy to manage queuing and crowding in mass transit systems

This paper solves the problem of optimizing a surcharge-reward scheme and analyzes equilibrium properties incorporating commuters’ departure time choice to relieve crowding and queuing congestion in mass transit systems. The surcharge-reward scheme incentivizes commuters to switch departure times from a pre-specified central period to shoulder periods. We formulate a bilevel model to design and optimize the surcharge-reward scheme. The upper-level problem minimizes the total equilibrium costs by determining the refundable surcharges, the rewards, and the corresponding central charging period. The lower-level problem determines the equilibrium of commuters’ departure times with respect to generalized travel costs. Equilibrium properties are analyzed and a sequential iterative solution algorithm is developed. We found that the existence of an optimal solution depends on the scheme design and there exists a lower bound on the surcharge to achieve the system optimum. Numerical studies are conducted on a commuting rail line in Copenhagen. The proposed algorithm converges efficiently, and the fare incentive scheme can simultaneously reduce the individual trip costs, total crowding costs, and total queuing time costs. The performance of the scheme increases with the rewards and surcharges up to a point and beyond which it stays unchanged.

A modelling platform for optimizing time-dependent transit fares in large-scale multimodal networks

With the continuous growth of urban areas around the world, overcrowding in large transit networks has become a persistent problem, with far-reaching impacts similar to those caused by congestion in large road networks. Moreover, instead of serving as a relief for large transportation systems, congested transit networks have increased delay-related traffic and transit costs. In light of these problems, cities seek cost-effective and relatively fast-to-implement strategies to mitigate transit system congestion, one of which is time-based fare structures. By implementing time-based fares, the transit demand may shift out of the congested peak periods, easing transit travel conditions. Although time-based fares are already in use in some transportation systems, their implementation is usually based on simplified what-if analyses. Such analyses of fare structures in previous studies have lacked a comprehensive evaluation of people's responses to these fares and is usually applied to simple or sometimes hypothetical transportation networks. Therefore, this paper presents a platform for analyzing and optimizing time-based transit fares in large networks, taking into consideration the effects of these fares on people's choices of mode, departure time, and route in addition to the interactions between transit vehicles and general traffic. As a case study, the largest metropolitan area in Canada, the Greater Toronto Area, is tested. The results show that the optimal time-based fares help spread the transit demand to the shoulders of the peak. However, the savings in weighted average multimodal door-to-door travel time over the whole network are slightly small compared to the large increase in peak-hour fares.

Ordered Generalized Extreme Value Model as a Tool for Demand Modelling of Discretionary Trips

Although the four-step model is the most common method in transportation demand modelling, it is exposed to a considerable criticism in terms of representing the actual choice behaviours of travellers. For example, the four steps are presented in a fixed sequence and independently from each other. Such assumption may be correct in case of obligatory trips (e.g. work trips) where travellers’ behaviour has usually no effect on trip generation or trip distribution stages. However, in discretionary trips, they may simultaneously decide on various trip dimensions. This paper tries to overcome the limitations of traditional four-step model associated with discretionary trips by using a joint discrete choice modelling approach that represents destination, departure time and travel mode choices under a unified framework. The proposed model to be used is the Ordered Generalized Extreme Value model where potential spatial correlation among discretionary destinations can be considered as well. The research methodology has been tested by using shopping and entertainment trips data of Eskisehir city in Turkey. The proposed framework seemed to be more effective and offered an accurate alternative to the first three stages of the traditional four-step model in a setting with a limited number of discretionary destinations.

Evaluating the Impacts of Parking App Services on Travellers' Choice Behaviour and Traffic Dynamics

As the products of intelligent transportation systems, parking apps have become convenient platforms for implementing parking policies, which can be provided as parking app services. This paper proposes a traffic simulation model for evaluating the impacts of parking app services on the travellers’ choice behaviour and traffic dynamics. Travellers are assumed to use three types of parking app services: the provision of information on real-time parking lot occupancies, parking reservation, and the display of dynamic parking fees. The behaviour of travellers, such as travellers’ mode choices, departure time choices, and learning behaviour, are considered in this model. Numerical experiments show that providing information on real-time parking lot occupancies can be helpful in reducing the use ratio of commercial parking lots, but the effect will ultimately be smoothed during the evolution of traffic dynamics. Moreover, parking reservation is an effective way to reduce travel costs and encourage travellers to choose park-and-ride. Furthermore, dynamic parking fees usually lead to the oscillation of traffic dynamics and travellers’ choices, in addition to an increase in travel costs. This model is a useful tool for analysing the impacts of other parking management policies that can be implemented as parking app services and can be a reference for evaluating the impacts of other parking polices.

Scheduling preferences and the value of travel time information

In this paper, we derive the value of a signal obtained by a traveller prior to the choice of departure time. The signal does not have to be a perfect prediction of the travel time. It is sufficient that it carries information about the travel time. The traveller may then consider the distribution of travel time conditional on the signal to increase her expected utility by making a better informed choice of departure time.We show that a signal always increases the expected utility compared to the situation without any signal. For a broad class of signals, the expected utility is monotone with respect to signal strength. Furthermore, we demonstrate that even perfect travel time information does not necessarily eliminate the cost of travel time variability and we establish necessary and sufficient conditions for when it does. We find that the predictable part of travel time variability may or may not be costly, depending on the shape of the traveller's scheduling utility at the origin of the trip. Using estimates of scheduling preferences from the literature, we show that the cost of predictable travel time variability may constitute a substantial part of the total cost of travel time variability. In a particular case of scheduling preferences, travel time distribution and noise distribution, we establish an analytic relationship between the strength of the signal and the expected utility of the trip and evaluate the marginal cost of signal weakness. This knowledge may facilitate design and cost-benefit analysis of traveller information systems and policies decreasing travel time variability.

Optimal congestion pricing with diverging long-run and short-run scheduling preferences

Traffic congestion is among the main market failures in modern cities. Dynamic marginal external cost pricing is the textbook economic response to this externality. Recent empirical work has shown that there is an important distinction between short-run departure time choice versus long-run routine formation of commuters, also characterized by differences in values of time and schedule delays for the short-run versus the long-run problem. This paper investigates whether this affects optimal pricing of congested roads. Using a dynamic model of congestion, and integrating it with a dynamic model of routine formation, it is found that contrary to expectation, short-run optimal congestion pricing alone cannot optimally decentralize the optimal formation of long-run routines. A separate instrument is therefore needed to optimize routine formation.

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.

A latent class joint mode and departure time choice model for the Greater Toronto and Hamilton Area

This paper presents a closed-form Latent Class Model (LCM) of joint mode and departure time choices. The proposed LCM offers compound substitution patterns between the two choices. The class-specific choice models are of two opposing nesting structures, each of which provides expected maximum utility feedback to the corresponding class membership model. Such feedback allows switching class membership in response to the changes in choice contexts. The model is used for an empirical investigation of commuting mode and departure time choices in the Greater Toronto and Hamilton Area (GTHA) by using a large sample household travel survey dataset. The empirical model reveals that overall 38% of the commuters in the GTHA are more likely to switch modes than departure times and 62% of them are more likely to do the reverse. The empirical model also reveals that the average Subjective Value of Travel Time Savings (SVTTS) of the commuters in the GTHA can be as low as 3 dollars if a single choice pattern of departure time choices nested within mode choices is considered. It can also be as high as 67 dollars if the opposite nesting structure is assumed. However, the LCM estimates the average SVTTS to be around 27 dollars in the GTHA. An empirical scenario analysis by using the estimated model indicates that a 50% increase in morning peak period car travel time does not sway more than 4% of commuters from the morning peak period.

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.

The habit-driven life: Accounting for inertia in departure time choices for commuting trips

This paper aims to explicitly account for the impact of inertia (or habit) on departure time decisions, and explore (1) to what extent departure time is influenced by inertia, (2) what influences individuals’ inertia with respect to departure time decisions, and (3) to what extent it impacts transport policies. We estimate an integrated choice and latent variable (ICLV) model using a stated preference survey for morning car commuters in the Greater Copenhagen Area. We interact the rescheduling components in the Scheduling Model (SM) with the latent variable Inertia. The modelling results show that higher levels of inertia yields higher rescheduling penalties and lower willing to shift departure time. Furthermore, we find that inertia in departure time is influenced by gender, presence of children in the household as well as work type. We test the behavioral responses to demand management policies for segments with different inertia, and find that the least inertial segment showed the highest substitution patterns, while the most inertial segment show the lowest substitution patterns. Finally, we compared the ICLV model to a reference model without inertia, and find that the effects of the demand management strategy is overestimated if inertia is neglected.

Modelling Departure Time, Destination and Travel Mode Choices by Using Generalized Nested Logit Model: Discretionary Trips

Despite traditional four-step model is the most prominent model in majority of travel demand analysis, it does not represent the potential correlations within different travel dimensions. As a result, some researches have suggested the use of choice modelling instead. However, most of them have represented travel dimensions individually rather than jointly. This research aims to fill this gap through employing the Generalized Nested Logit model for jointly representing three major travel dimensions; destination, departure time and travel mode. The suggested research methodology depends mainly on agglomerating alternatives that have similar error term’s variances within specific gaps under common nests without any imposed restrictions. Moreover, different variance gaps lead to overlapped nesting system which can enable analysers modelling inner and inter-correlation. The proposed approach has been examined through modelling individuals’ choices among the main shopping destinations in Eskisehir city, Turkey. In the light of estimation results, the proposed model attains a relatively good over-all goodness of fit which reflects a more prominent predictability power. Moreover, individuals in Eskisehir have been found perceiving more interest to the cost rather than time. From another hand, a behaviour of trading-off between performing such trips at peak periods by using transit or making them at off-peak by private car has been detected.

Using Clustering Methods in Multinomial Logit Model for Departure Time Choice

Travellers have to make some decisions for each trip, and one of them is the choice of departure time. Discrete choice models have been employed as an approach to departure time modelling by many researchers. In this method, preparing choice set is a primary challenge which involves the definition of some departure periods to be selected by the traveller. In this research, choice sets were formed by applying the clustering methods on departure times. Afterwards, we developed Multinomial Logit (MNL) models on different choice sets and compared the models. The data used throughout this research belonged to Mashhad City. Research results indicated that Ward’s hierarchical clustering method is improper for time discretization; furthermore, the K-means clustering method is more efficient than the expectation maximization and K-medoids methods in the time discretization for MNL modelling. The developed model (based on K-means clustering method) accurately predicts departure time for 58% of persons within the test group, which reflects the effectiveness of the resulting model compared to the 36% which is obtained without the model.

Stable Day-to-Day Dynamics for Departure Time Choice

All existing day-to-day dynamics of departure time choice at a single bottleneck are unstable, and this has led to doubt over the existence of a stable user equilibrium in the real world. However, empirical observations and our personal driving experience suggest stable stationary congestion patterns during a peak period. In this paper, we attempt to reconcile the discrepancy by presenting a stable day-to-day dynamical system for drivers’ departure time choice at a single bottleneck. In our model, the decision variable in the execution stage is still drivers’ departure times on the next day, but in the planning stage before the execution stage, drivers determine their departure times in order to arrive at the destination at better times with lower scheduling costs. We first define within-day traffic dynamics with the point queue model, costs, the departure time user equilibrium (DTUE), and the arrival time user equilibrium (ATUE). We then identify three behavioral principles in the planning stage: (i) drivers choose their departure and arrival times in a backward fashion (backward choice principle); (ii) after choosing the arrival times, they update their departure times to balance the total costs (cost-balancing principle); (iii) they choose their arrival times to reduce their scheduling costs or gain their scheduling payoffs (scheduling cost–reducing or scheduling payoff–gaining principle). In this sense, drivers’ departure and arrival time choices are driven by their scheduling payoff choice. With a single tube or imaginary road model, we convert the nonlocal day-to-day arrival time shifting problem to a local scheduling payoff shifting problem. After introducing a new variable for the imaginary density, we apply the Lighthill–Whitham–Richards (LWR) model to describe the day-to-day dynamics of scheduling payoff choice and present splitting and cost-balancing schemes to determine arrival and departure flow rates accordingly. We define the scheduling payoff user equilibrium (SPUE) as the stationary state of the LWR model, formulate a new optimization problem for the SPUE, and prove the global stability of the SPUE and, therefore, ATUE and DTUE by using Lyapunov’s second method in which the objective function in the optimization formulation is the potential function. We also develop the corresponding discrete models for numerical solutions and use one numerical example to demonstrate the effectiveness and stability of the new day-to-day dynamical model. Different from existing ones, the new adjustment mechanism leads to stable day-to-day departure time choice dynamics by guaranteeing that drivers have better choices of departure/arrival times with larger scheduling payoffs on the next day, and such better choices are not over-chosen because of the constraint imposed by the single tube’s cross-section area, which is equal to the jam density in the LWR model. This study is the first step for understanding stable day-to-day dynamics for departure time choice, and many follow-up studies are possible and warranted.

Modeling Concurrent Day-to-Day Departure Time and Route Choices With Multiple Micro-Preferences

Day-to-day traffic dynamics is to model the day-to-day evolution of travelers' travel choices, which helps to understand the aggregate traffic evolution of a non-equilibrium network and then develop scientific managements. This topic is attracting increasing interests. In the literature, day-to-day route and departure time choices are usually addressed separately; and the existing models commonly adhere to the rational behavior adjustment criterion (RBAC), but pay little attention to extra behavior preferences. In this article, we formulate the day-to-day departure time and route choices in a united model. Moreover, besides the RBAC, three microscopic behavior preferences (i.e., simplicity-seeking, proximity-prone and marginal cost preference) are suggested for modeling. The problem is formulated as a discrete-time dynamics named by the day-to-day departure time and route adjustment process (DTRAP). Basic properties of the model are verified theoretically; and numerical results indicate that the suggested micro-preferences have significant impact on traffic evolution. Thus, serious treatment and more in-depth research attention need to be laid on these micro-preferences for obtaining scientific understanding on the day-to-day network traffic evolution or for avoiding ineffective (or even wrong) traffic managements.

Inter- and Intrapersonal variation in destination choice

This paper examines spatial-temporal inter- and intrapersonal variation in destination choice, based on longitudinal smartphone data for the Netherlands. Mixed logit destination choice models were estimated using two years of data (2014 and 2015) from the Dutch Mobile Mobility Panel, in which over 68,000 trips for 442 respondents were recorded with a smartphone app during an annual four-week measurement period. A distinction was made between trips to compulsory activities (such as work) and trips for discretionary purposes (such as recreation) as they are associated with different trip characteristics. Discrete destination alternatives were defined based on individuals’ behaviour in terms of repeatedly visited destinations and the statistical distribution of a spatial repetition index. The model results show that intrapersonal variation in destination choice, departure time and mode choice was relatively high for less frequently visited locations, which indicates novelty-seeking behaviour in destination choice. Furthermore, we found a strong connection between activity, departure time, and destination choice. And, mode choice and departure time choice were highly repetitive for destinations visited repetitively (e.g. work), but not for discretionary activities.

Departure Time and Route Choices With Accurate Information Under Binary Stochastic Bottleneck Capacity in the Morning Commute

Uncertainty, a critical factor of causing congestion and extra travel costs in the commute, can be mitigated by providing information. This paper studies the welfare effects of accurate pre-trip information on departure time and route choices in the morning commute under binary stochastic bottleneck capacity. We consider a classical two-route network. Each route has a single bottleneck where congestion occurs during the rush hours. The two routes’ bottleneck capacities vary from day-to-day due to events such as bad weather, accidents, and temporary road closures. We derive all equilibrium solutions in consideration of the differences between routes in free-flow travel time, the shadow value of travel time, the severity of bottleneck capacity reductions, and the degree of correlation between two routes in travel conditions. Furthermore, we investigate the benefit changes from zero-information to full-information and prove that accurate pre-trip information about the bottleneck conditions is strictly welfare-improving. Finally, these theoretical results are supplemented by case studies that show examples of benefit gains from pre-trip information.

A multi-dimensional rescheduling model in disrupted transport network using rule-based decision making

Abstract Apart from daily recurrent traffic congestion, unforeseen events such as flood induced road damages or bridge collapses can degrade the capacity of traffic supply and cause a significant influence on travel demand. An individual realising the unexpected events would take action to reschedule its day plan in order to fit into the new circumstance. This paper analyses the potential reschedule possibilities by augmenting the Within-Day Replanning simulation model implemented in the Multi-Agent Transport Simulation (MATSim) framework. Agents can adjust day plan through multi-dimensional travel decisions including route choice, departure time choice, mode switch, and trip cancellation. The enhanced model not only improves the flexibility of MATSim in rescheduling a plan during an execution day, but also lays the foundation of integrating more detailed heterogeneity decision rules into the travel behaviour simulation to cope with unexpected incidents. Furthermore, the proposed rescheduling model is capable of predicting the network performance in the real-world picture and gives a hint on how best react to transport disruptions for transport management agency.

Departure Time Choice and Bottleneck Congestion with Automated Vehicles: Role of On-board Activities

It is widely expected that automated vehicles (AVs) will revolutionise travel experience by better facilitating various on-board activities. While these activities could make travel more pleasant, as is often supposed, they could also affect daily schedules, the related travel choices, and finally, the aggregate travel patterns – possible influences that are still insufficiently studied. For example, a morning commuter deciding to perform some home or work activities during travel, instead of at home or work, could also reconsider his departure time to work. More such travellers together could reshape traffic congestion. This paper models exactly this scenario. It formulates new scheduling preferences, which account for home and/or work activities during morning commute, and uses these (1) to analyse the optimal departure times when there is no congestion, and (2) to obtain the equilibrium congestion patterns in a bottleneck setting. If there is no congestion, it is predicted that AV users would depart earlier (later), if the on-board environment supports their home (work) activities. If there is congestion, AV users that perform home (work) activities during travel skew the congestion to earlier (later) times, and AV users that perform both activities increase both early and late congestion. Engaging in any activity during travel worsens congestion, at least when assuming that AVs do not increase bottleneck capacity. If future AVs would be specialised to support only home, only work, or both home and work activities, and would do so to a similar extent, then ‘Work AVs’ would increase the congestion the least.

A QUADRATIC PROGRAMMING APPROACH FOR SOLVING A DYNAMIC USER EQUILIBRIUM WITH SIMULTANEOUS DEPARTURE TIME AND ROUTE CHOICE

本研究は，経路と出発時刻を同時に選択する動的利用者均衡配分 (DUE-SDR: dynamic user equilibrium with simultaneous departure time and route choice)モデルの解法を開発する．具体的には，まず，一起点多終点もしくは多起点一終点の一般ネットワークを対象とし，待ち行列の物理的長さを捨象したpoint-queueモデルを用いて渋滞を表現する枠組を示す．この枠組の下で，DUE-SDRモデルを混合線形相補性問題として定式化する．次に，こうして定式化された問題が，適当な離散時点の枠組下で等価な二次計画問題に帰着することを明らかにし，これをFrank-Wolfe法を用いて解くアルゴリズムを開発する．最後に，提案解法をSioux-Fallsネットワークに適用し，未知変数が数万個に及ぶ問題に対しても，実用的な時間内に均衡解が求められることを示す．