Departure Time Choice Research Articles

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.

A new look at departure time choice equilibrium models with heterogeneous users

This paper presents a systematic approach for analyzing the departure-time choice equilibrium (DTCE) problem of a single bottleneck with heterogeneous commuters. The approach is based on the fact that the DTCE is equivalently represented as a linear programming problem with a special structure, which can be analytically solved by exploiting the theory of optimal transport combined with a decomposition technique. By applying the proposed approach to several types of models with heterogeneous commuters, it is shown that (i) the essential condition for emerging equilibrium “sorting patterns,” which have been known in the literature, is that the schedule delay functions have the “Monge property,” (ii) the equilibrium problems with the Monge property can be solved analytically, and (iii) the proposed approach can be applied to a more general problem with more than two types of heterogeneities.

Analysis of the interaction among destination and departure time choices

Analysis of the interaction among destination and departure time choices

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

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

Is providing inaccurate pre-trip information better than providing no information in the morning commute under stochastic bottleneck capacity?

One way to eliminate the negative impact of travel time uncertainty faced by commuters is to provide them with traffic information so that they can make informed travel. Nevertheless, the provided information may not be accurate, thereby affecting commuters’ choices as well as system efficiency. This paper investigates the welfare effects of inaccurate pre-trip information on commuters’ departure time choice under stochastic bottleneck capacity in the morning commute. Three cases concerning commuters’ actions in response to inaccurate pre-trip information, namely compliance, noncompliance, and co-existence, are studied. We consider two types of traffic conditions: good conditions and bad conditions, as well as errors in forecasting good conditions and bad conditions, respectively. We derive all theoretical solutions under different magnitudes of forecast errors, and frequency and severity of bottleneck capacity reductions in the compliance and noncompliance cases, and adopt an iterative algorithm to obtain the departure flow patterns in the co-existence case. Our results show that the benefits of inaccurate information depend on information quality, commuters’ response and heterogeneity, and the frequency and severity of bottleneck capacity reductions. A sensitivity analysis is carried out to further evaluate the benefit gains/losses under different situations in the three cases.

Air travellers’ behaviour when choosing airline and flight departure time: The case of medellín, colombia

The behaviour of air travellers and their choice of airline and flight departure time are relevant subjects with an increasing interest because of their implications for planning purposes. However, most of the current approaches consider them to be isolated choices, while just a few contemplate them as a joint choice. The present investigation modelled the choice process of individuals related to a set of alternatives formed by a combination of airline and flight departure time, in the context of domestic travellers in the Metropolitan Area of Medellín, Colombia. The approach specifies discrete choice models, estimated from face to face surveys of revealed preferences data collected in the two airports serving the city. Correlations between alternatives and substitution patterns are calculated, including subjective values of time, direct and cross elasticities for ticket cost, travel time and departure time attributes. We also discuss some policy scenarios. According to the results, flights operating at a similar time, and those offered by airlines with the highest market presence have a higher level of competition. Travellers for leisure purposes are less sensitive to travel time than those travelling for business or another motive. Furthermore, the difference between the actual departure time and the preferred departure time of the traveller affects negatively the probability of choosing a specific alternative. In Colombia, air travellers have a highly elastic behaviour towards airfares, travel time and flight departure time. The conclusions drawn by the interpretation of the proposed models are useful for authorities in the formulation of policies, and for aviation companies to optimise their operation. The research provides valuable information on the context of Colombian air transport, which is poorly reviewed in the literature.

Comparison of Clustered-based MNL and Duration Models in Departure Time Choice Modelling

Comparison of Clustered-based MNL and Duration Models in Departure Time Choice Modelling

A time-dependent shared autonomous vehicle system design problem

The emergence of the shared autonomous vehicle (SAV) provides new opportunities and challenges for the fashionable car-sharing mode. This study proposes a time-dependent SAV system design problem by jointly optimizing fleet size, parking infrastructure deployment, and daily operation of the system for infrastructure planning in the long run. The dynamic system optimum (DSO) principle in terms of total daily system cost (TDSC) is adopted to formulate the daily operation of the SAV system, i.e., users’ departure time choices and SAVs’ route choices. By incorporating the link transmission model (LTM) as the traffic flow model, the daily operation problem (DOP) of the SAV system is formulated as a linear programming (LP) problem. Further, the time-dependent SAV system design problem is formulated as a mixed integer linear programming (MILP) problem. The LP relaxation of the proposed MILP problem could provide a tight lower bound, and a diving heuristic algorithm is developed to solve the proposed MILP problem. Finally, numerical examples are designed to illustrate the properties of the model and the efficiency of the proposed solution algorithm.

Expanding the analysis scope of a MATSim transport simulation by integrating the FEATHERS activity-based demand model

MATSim is an agent-based transport simulation model. In contrast to a pure dynamic traffic assignment (DTA) model, MATSim can react to more choice dimensions than route choice, which is the only choice treated by a typical DTA. Simulating the mobility and activity participation of individuals during the whole day, MATSim can additionally represent mode choice, departure time choice, and other decisions and is, therefore, policy-sensitive in terms of these choice dimensions. This allows for the analysis of a wide scope of policies. MATSim can, however, not model the choice of the sequence of activity participation nor the choice of activity participation as such. Also, choices of locations are not typically part of the modeling scope. Interventions into the transport and land-use systems may, however, be substantial such that they can effect changes in behavior in terms of these choices. To allow for the assessment of such reactions, the FEATHERS activity-based demand model is coupled with MATSim. This paper explores different options of integration and describes the development steps of the integration of FEATHERS and MATSim.

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.

Model formulation and calibration procedure for integrated multi-modal activity routing and network assignment models

In this paper, a novel transport planning model system (TPMS) is formulated built on the concepts of network, multi-modality, integrity and instant calibration. In the proposed formulation, activity-travel pattern (ATP) choice elements including the choices of activity, activity sequence, mode, departure time, and parking location, are all unified into a time-dependent ATPs generator. The proposed model accounts for the dynamicity of the network, including time-of-day and congestion effects in a joint structure for transport supply and demand. Moreover, the proposed TPMS explicitly formulates an operating capacitated public transport system. To allow visiting locations multiple times and to alleviate the complexity of the proposed model, a novel multi-visit vehicle routing problem is proposed which does not enumerate the node and link visits. In order to calibrate the model based on the major travel attributes of the travel survey data, a set of splitting ratios are introduced to distribute trips on the network. The model uses the splitting ratios to integrate the ATPs generator and the traffic assignment (TA) model in a unified TPMS structure. The effectiveness of the proposed structure is demonstrated through numerical examples provided.

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.

Analysis of adding-runs strategy for peak-hour regular bus services

This paper proposes an adding-runs strategy to alleviate in-vehicle crowding for peak-hour bus services. Passengers’ departure time choices under user equilibrium and system optimum conditions are investigated with and without adding-runs strategy. A bi-level programming model is developed to determine the optimal adding-runs strategy. An artificial bee colony algorithm is adopted to solve the proposed bi-level problem. Numerical examples show that the adding-runs strategy is effective in alleviating crowding effects and reducing schedule delay in peak-hour bus services. The total system cost can be reduced by more than 8% with the optimal adding-runs strategy.

An activity-based bottleneck model with stochastic capacity

This study investigated commuters’ departure time choice with stochastic capacity. It has typically been solved by using Vickrey’s bottleneck model which doesn’t take the impact of commuters’ activity on decision-making into account. We added activity utility parameters to Vickrey’s bottleneck model. Travel costs became negative utility. The model considered home-activity utility before work and work-activity utility in the morning rush. Simultaneously, the capacity of the bottleneck changed randomly between a designed value (good condition) and a degraded one(bad condition). We derived six reasonable equilibrium patterns by maximizing the commuters’ activity utility and closed form solutions to all possible equilibrium patterns. The departure rate curve for each equilibrium pattern was made within a reasonable parameter range.

Dynamic Combined-Mode Traffic Network Model considering Transfer Behaviors

We propose a dynamic combined-mode traffic network model considering transfer behaviors. We assume that travelers can be classified into two classes: one class is pure-mode travelers who complete a trip by single transportation mode, and another is combined-mode travelers who cover a journey by car, bus, and so forth. The multimode point queue model is used to model the interaction of cars and buses on the network. We present an integrated variational inequality formulation to capture the complex traveler choice behaviors such as departure time choices, transfer point, and route choices. Finally, a numerical example is given to illustrate the effectiveness of the proposed heuristic algorithm and model.

Hierarchical density-based clustering methods for tolling zone definition and their impact on distance-based toll optimization

Real-time network control strategies such as congestion pricing have been used in a number of metropolitan areas around the world for traffic congestion mitigation. Recent advances in Global Navigation Satellite System (GNSS) technology have led to increasing interest in distance- or usage-based road pricing as an effective alternative to traditional facility-, cordon- and area-based pricing that typically rely on fixed infrastructure. In this paper, we propose the use of feature-variant clustering methods, OPTICS and HDBSCAN*, as a systematic approach for tolling zone definition to operationalize distance-based tolling schemes. Subsequently, we develop a framework for predictive distance-based toll optimization to evaluate network performance for the various tolling zone definitions derived from the aforementioned feature-variant clustering methods. In this framework, for a specific tolling zone definition, tolling function parameters are optimized using a simulation-based Dynamic Traffic Assignment (DTA) model operating within a rolling horizon scheme. Predictive optimization is integrated with the guidance information generation. Behavioral models capture drivers’ responses to the tolls in terms of trip cancellation and choices of mode, route and departure time. Experiments on the real-world Expressway and Major Arterials network of Singapore demonstrate improved effectiveness of distance-based toll optimization given tolling zone definitions derived from feature-variant clustering, compared to fixed cordon-based pricing, adaptive cordon-based pricing, as well as distance-based pricing with ad hoc tolling zone definitions. Further, the results indicate that the use of the marginal link cost tolls as a clustering feature produces the most robust tolling zone definitions and yields significant improvements in social welfare over ad hoc zone definitions and cordon-pricing. Finally, experiments on the Boston CBD network also demonstrate the effectiveness of distance-based toll optimization schemes on urban traffic networks.

Fare incentive strategies for managing peak-hour congestion in urban rail transit networks

In urban rail transit (URT) systems, fare incentives are emerging as a method to manage peak hour congestion. In this study, we propose a practical framework to model the departure time and route choice of URT passengers during peak hours. First, various attributes that influence the departure choice of passengers are investigated, and the willingness of passengers to shift their departure time or routes is evaluated based on a questionnaire survey of passengers of the Shanghai metro. Then, a discrete choice model is used to identify the interrelationships between fare incentives and the choice behaviors of passengers. We propose the following two fare incentive strategies: a time-based fare incentive strategy (TBFIS); and a route-based fare incentive strategy (RBFIS). These strategies consider changes in both time and space. Finally, the effectiveness of the two different fare incentive strategies is evaluated using the Shanghai URT network simulation system.

Scheduling preferences, parking competition, and bottleneck congestion: A model of trip timing and parking location choices by heterogeneous commuters

This study examines the effects of a time-varying congestion toll and a location-dependent parking fee on the behavior of heterogeneous commuters and their commuting costs. To this end, we develop a model of departure time and parking location choices by heterogeneous commuters and characterize its equilibrium. By comparing the equilibrium with and without pricing policies, we obtain the following results: (1) imposing a parking fee and expanding parking capacity may concentrate the temporal distribution of traffic demand, thereby exacerbating traffic congestion; (2) the expansion of parking capacity does not necessarily lead to a Pareto improvement when a parking fee is not imposed; (3) the social optimum is achieved by combining a parking fee with a congestion toll; and (4) the revenue obtained from pricing of roads and parking exactly equals the costs for optimal bottleneck and parking capacities, respectively; that is, the self-financing principle holds separately for bottleneck capacity and parking capacity.

Joint estimation of mode and time of day choice accounting for arrival time flexibility, travel time reliability and crowding on public transport

This study develops joint choice models of mode and departure time for implementation in MetroScan, a new version of TRESIS (Hensher and Ton, 2002). Separate models are estimated for work and non-work purposes, testing all practical alternatives of model structure with a rich set of explanatory variables. The contributions of the current work to the existing TRESIS are twofold. First, travel demand for non-work purposes such as shopping, social and recreation are explicitly modelled in MetroScan as opposed to TRESIS that scales the demand for work purposes to obtain non-work travel demand. Second, choices of travel mode and departure time become more sensitive to situational factors such as the flexibility of arrival time, the reliability of travel time and crowding. Willingness to pay for various improvements to the level of service is derived. We describe and demonstrate how the proposed models are applied in the general modelling framework of MetroScan.

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.