Preferred Arrival Time Research Articles

A Paradox of Telecommuting and Staggered Work Hours in the Bottleneck Model

We study the long- and short-term effects of telecommuting (TLC), staggered work hours (SWH), and their combined scheme on peak-period congestion and location patterns. In order to enable a unified comparison of the schemes’ long- and short-term effects, we develop a novel equilibrium analysis approach that consistently synthesizes the long-term equilibrium (location and percentage of telecommuting choice) and short-term equilibrium (preferred arrival time and departure time choice). By exploiting their special mathematical structures similar to optimal transport problems, we derive the closed-form solution to the long- and short-term equilibrium while explicitly considering their interaction. These closed-form solutions elucidate the discrepancies between the effects of each scheme and uncover a paradoxical finding: the introduction of SWH, in conjunction with TLC, may increase the total commuting costs compared with the scenario with only TLC, without yielding any improvement in worker utility. History: This paper has been accepted for the Transportation Science Special Issue on ISTTT25 Conference. Funding: This work was supported by Council for Science, Technology and Innovation (CSTI), Cross-ministerial Strategic Innovation Promotion Program (SIP), the 3rd period of SIP “Smart Infrastructure Management System” [Grant JPJ012187] (Funding agency: PublicWorks Research Institute, Japan). This work was also supported by Japan Society for the Promotion of Science (JSPS) KAKENHI [Grants JP20J21744, JP21H01448, JP24K00999, JP20K14843, and JP23K13418] and the Support Program for Urban Studies of the Obayashi Foundation. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2024.0520 .

Shared e-scooters: A last-minute mode?

In this paper we assess whether shared e-scooters are being used in hurried situations to avoid arriving late, making trips we define as last-minute trips. We identify trip characteristics for last-minute trips using data from shared e-scooters in Stockholm. Trip arrival time patterns, distances, durations, and speeds are analyzed in relation to desired arrival times. We observe a peak in the number of trips ending before the full hour and a smaller peak of trips ending before the half hour of weekday mornings. These trips have lower average trip durations and higher average speeds, indicating a larger share of last-minute trips. Arrival patterns shift when the assumed preferred arrival time changes, and when the risk associated with arriving late increases. Furthermore, there is an increased share of trips that started close to public transportation around the full hour, indicating that a portion of last-minute trips may also be last-mile trips. Our results show that while last-minute trips are infrequent per user, they do make up a relatively large share of weekday morning trips. These results highlight an important market segment for shared e-scooter services and may also explain why some research reports e-scooter usage replacing walking.

Managing congestion at the entrance of subway stations with reserved inbound service

During peak hours at many entrances of subway stations, huge queues of passengers try to enter. To address this congestion, the city of Beijing has introduced a reserved inbound services in several subway stations. Passengers can reserve a time interval via which they can enter with reduced queuing. We aim to analyse and optimize this system while modelling the congestion and choices of travellers using a Vickrey bottleneck model approach. We develop a reserved system where there can be a different number of reservation time intervals before and after the preferred arrival time, and for common preferences for schedule delay adding intervals before the preferred arrival time reduces travel costs more than adding them after it. Results show that the time interval set can influence both implementation conditions and the efficiency of the reserved system. Suggestions about the time interval set are given. Passengers’ inbound choices with different inbound peak reservation costs are analysed.

A Multi-Stage Approach for External Trucks and Yard Cranes Scheduling with CO2 Emissions Considerations in Container Terminals

Background: In container terminals, optimizing the scheduling of external trucks and yard cranes is crucial as it directly impacts the truck turnaround time, which is one of the most critical performance measures. Furthermore, proper scheduling of external trucks contributes to reducing CO2 emissions. Methods: This paper proposes a new approach based on a mixed integer programming model to schedule external trucks and yard cranes with the objective of minimizing CO2 emissions and reducing truck turnaround time, the gap between trucking companies’ preferred arrival time and appointed time, and the energy consumption of yard cranes. The proposed approach combines data analysis and operations research techniques. Specifically, it employs a K-means clustering algorithm to reduce the number of necessary truck trips for container handling. Additionally, a two-stage mathematical model is applied. The first stage employs a bi-objective mathematical model to plan the arrival of external trucks at the terminal gates. The second stage involves a mathematical model that schedules yard cranes’ movements between different yard blocks. Results: The results show that implementing this methodology in a hypothetical case study may lead to a substantial daily reduction of approximately 31% in CO2 emissions. Additionally, the results provide valuable insights into the trade-off between satisfying the trucking companies’ preferred arrival time and the total turnaround time. Conclusions: The integration of data clustering with mathematical modeling demonstrates a notable reduction in emissions, underscoring the viability of this strategy in promoting sustainability in port-related activities.

Estimating the Value of Airport Access Time in Developing Countries with a Case Study of Nanjing, China

The valuation of time is one of the most important public policy issues in project cost-benefit analysis. This paper estimates the value of airport access time and time variability in developing countries with a case study of Nanjing, China. An international meta-analysis is being used to identify the factors that may affect heterogeneity in the value of travel time. Regression models are then established for the prediction of the value of travel time. The results provide some new insights into the impacts of survey region, traffic mode, and trip purpose on the value of travel time. Considering the significant influencing factors that were obtained, stated preference surveys are designed and used to collect data on preferred arrival time and decision choice under various hypothetical situations. A multivariate regression model is used with the data to explore the significant factors that influence the travelers’ preferred arrival time. Mixed logit models are developed to estimate the value of airport access time, value of schedule delay early, and value of schedule delay late by incorporating the effects of travel delay variability on users’ scheduling costs. The tax system is being used to illustrate the contribution of different income groups to social funds, which also calculates the social value of airport access time, social value of schedule delay early, and social value of schedule delay late. The results identify the significant factors that may affect the valuation of airport access time and provide reasonable estimates for these values. The findings also bring new enlightenment on the effects of the variation of airport access time.

Reducing external container trucks’ turnaround time in ports: A data-driven approach under truck appointment systems

A longer turnaround time of container trucks in ports are commonly related to a higher energy cost and more carbon emissions, hindering the green development goal of ports. In this context, the implementation of Truck appointment system (TAS) is widely recognized as a very effective way to reduce external container trucks’ total turnaround time in ports. However, the number of appointment quotas allocated for each time window are the key to determine the efficiency of a TAS. This study proposed a data-driven approach to address the appointment quota optimization problem, which combined data mining technologies with mathematical optimization modeling methods, and an empirical study in YT port, China was given. Specifically, the smart gate data of the port were mined to explore the causal relationship between the number of truck arrivals at each time window and their total turnaround time within the port. The results exhibited a non-deterministic quadratic function relationship, and the range of changes regarding total turnaround time were measured by boundary functions obtained through a stepwise approaching algorithm. Considering the obtained causal relationship and its uncertainties, a robust optimization model was built to find the optimal appointment quota plan by minimizing the total turnaround time of external container trucks and the total deviations from the trucks’ preferred arrival time in the worst case. A numerical experiment was designed to examine the validity of the proposed data-driven approach, and compare the effectiveness of the proposed approach with a conventional stochastic optimization method. The results indicated the proposed approach was more effective on reducing the external container trucks’ total turnaround time in ports under a Truck appointment system.

MILP models for the Dial-a-ride problem with transfers

Automated vehicles are becoming a reality. Expectations are that AVs will ultimately transform personal mobility from privately owned assets to on-demand services. This transformation will enhance the possibility of sharing trips, leading to shared AVs (SAVs). The preeminent aim of this paper is to lay foundations for fast and efficient algorithms to be used in such new driving conditions. These algorithms must be able to solve Dial-a-Ride problems with transfers (DARPT). Hence, they should efficiently assign passengers to vehicles and routes while also: administering vehicles dispatch, determining convenient parking for idling vehicles and managing vehicle routing in real-time. In this paper, we develop two integer linear programming models (one in continuous time and one in discrete time) and their extensions to solve the DARPT. Our models take into account routing, service times, constraints on maximum route time-span, unserved requests, preferred arrival and departure time, nonconstant travel times, convenient parking while optimizing routing costs and quality of the service. The models are tested on instances based on Google Maps data by solving them with a commercial solver. The results of these tests are the starting point for validating the performance of forthcoming, ad hoc metaheuristics to be used in real-life sized scenarios. • Introduction of a continuous and discrete time model for the DARP with transfers. • A formulation for cycles that does not rely on dummy nodes. • Introduction of ‘moves’ to connect continuous and discrete variables. • Generation of real-life benchmark instances.

Tri-reference-point hypothesis development for airport ground access behaviors

Studies have applied single-reference-point or safety margin hypotheses to examine how advanced traveler information affects travel behaviors. However, these theories may fail to fully capture the trade-offs among origin departure time, airport access time, and terminal processing time in terms of airport ground access behaviors. In this study, we developed a tri-reference-point hypothesis and assumed that the rate of change of utility may change at the air passenger’s preferred (PAT), earliest acceptable (EAT), and latest acceptable (LAT) airport arrival times. With an empirical data set collected from 304 passengers at Taipei Songshan Airport, the study examined the tri-reference-point hypothesis by analyzing airport ground access mode choice behaviors with a pooled framework that combined revealed and stated preferences. Moreover, the study developed four alternative specifications for schedule delay variables, assuming that air passengers used different reference points to determine relative gains and losses of the expected airport arrival time. The specifications included selecting both EAT and LAT as the zero-utility points (an indifference-band specification) and either one of PAT, EAT, and LAT as the single zero-utility point. Regardless of which specification was employed for schedule delay variables, the tri-reference-point hypothesis was generally supported. In particular, a significant difference of the rate of change of utility around PAT, EAT, and LAT was identified in the analysis results. When managing increasing road travel times and increasingly congested terminals, air passengers were more willing to retime their origin departure time to an earlier time than to switch their ground access mode. The implications of the analysis results for airport ground access management are discussed in the study.

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.

The impact of delays on the welfare effects of on-track competition: The case of transfer passengers with operator-tied tickets

When connecting trains may be missed due to delays, and passengers are insufficiently flexible due to operator-tied ticketing, on-track competition may reduce effective frequency. We analyze passengers who share α−β−γ− preferences for being on time and a price-sensitive demand, but differ in the preferred arrival time. If the probability of missing a connection due to a delay is sufficiently high, both producer and consumer surplus in a duopoly with reduced effective frequency is smaller than in the monopoly case. Apart from reducing unpunctuality, ensuring the transferability of tickets, and switching to competition for the market, may constitute (regulatory) remedies.

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.

A simulation-based optimization approach for external trucks appointment scheduling in container terminals

ABSTRACT In Container Terminals (CTs), many containers are daily delivered by a large number of External Trucks (ETs) which lead to several issues such as long waiting times at gates and yards, harmful emissions, and low productivity of the CTs. To resolve these issues, truck appointment systems are used to schedule appointments of ETs and achieve more balance in CTs’ workload. Most of the studies have focused on deterministic modelling of the ETs’ arrival process with considering yard operations or gate operations. Also, a little effort has been devoted to collaboration between trucking companies and CTs when scheduling ETs’ appointments. Unlike previous studies, this paper presents a simulation-based optimization approach to collaboratively schedule ETs’ appointments with considering yard and gate operations as well as their stochastic natures. The proposed approach integrates a simulation model with an MIP model with objective of minimizing turnaround times of ETs and inconveniences resulting from shifting the arrivals of ETs away from their preferred arrival times. The proposed approach is validated against an approach from literature. In addition, its performance is investigated by solving artificial instances inspired by real data. A framework for implementing the proposed system in the IoT-based container terminals is also developed.

An online shortest path algorithm for reliable routing in schedule-based transit networks considering transfer failure probability

Most schedule-based transit assignment models assume deterministic vehicle arrival/departure times, in contrast to random vehicle arrival assumption in frequency-based models. This assumption simplifies user route choice behavior and fails to capture users’ response to unreliable service. Moreover, the inherent inconvenience of transfers, primarily due to uncertain waiting time and failure probability, has always been a challenge in modeling transit networks, to the extent that determining an appropriate transfer penalty for heterogeneous user population is still a question when using planning models. This study aims to bridge the gap between frequency-based and schedule-based transit assignment models, and proposes a path algorithm in schedule-based transit networks with stochastic vehicle arrival times to model users’ adaptive behavior in response to unreliable service. Path reliability is modeled by link failure probability, and an online shortest path algorithm is developed to find a routing policy with minimum expected travel time given preferred arrival time to destination. Complexity analyses and computational tests indicate that the model has potential for application in large-scale transit networks. Numerical tests verify that the model assigns passengers to more reliable paths with lower transfer rate without a need for a large transfer penalty.

Equilibrium in a dynamic model of congestion with large and small users

Individual users of transportation facilities often control a significant share of total traffic. Examples include airlines, rail and maritime freight shippers, urban goods delivery companies, taxi companies, and ride-sharing companies. These users have an incentive to internalize the congestion delays their own vehicles impose on each other by adjusting the timing of their trips. We investigate simultaneous trip-timing decisions by large users and small users in a dynamic model of congestion. Unlike previous work, we allow for heterogeneity of trip-timing preferences and for the presence of small users such as individual commuters and fringe airlines. We derive the optimal fleet departure schedule for a large user as a best-response to the aggregate departure rate of other users. We show that when the vehicles in a large user’s fleet have sufficiently diverse preferred arrival times, there may exist a pure-strategy Nash-equilibrium (PSNE) in which the large user schedules vehicles when there is a queue. This highlights that the problem of non-existence of a PSNE identified in Silva et al. (2016) for the case of symmetric large users hinges on the assumption that all vehicles in each fleet have the same preferred arrival time. We also develop some examples to identify under what conditions a PSNE exists, and when the large user departs. The examples illustrate how self-internalization of congestion by a large user can affect the nature of equilibrium and the travel costs that it and other users incur.

The electric autonomous dial-a-ride problem

In the Dial-a-Ride-Problem (DARP) a fleet of vehicles provides shared-ride services to users specifying their origin, destination, and preferred arrival time. Typically, the problem consists of finding minimum cost routes, satisfying operational constraints such as time-windows, origin-destination precedences, user maximum ride-times, and vehicle maximum route-durations. This paper presents a problem variant for the DARP which considers the use of electric autonomous vehicles (e-ADARP). The problem covers battery management, detours to charging stations, recharge times, and selection of destination depots, along with classic DARP features. The goal of the problem is to minimize a weighted objective function consisting of the total travel time of all vehicles and excess ride-time of the users. We formulate the problem as a 3-index and a 2-index mixed-integer-linear program and devise a branch-and-cut algorithm with new valid inequalities derived from e-ADARP properties. Computational experiments are performed on adapted benchmark instances from DARP literature and on instances based on real data from Uber Technologies Inc. Instances with up to 5 vehicles and 40 requests are solved to optimality.

Temporal framing of stated preference experiments: does it affect valuations?

In this paper, we explore how valuations of trip attributes by train commuters differ between a short-run (departure time choice) and a long-run (travel routine choice) context using a unique SP experiment explicitly designed for this purpose. In the short-run version of the SP experiment, the respondents receive information about available travel options shortly before they had planned to travel. In the long-run version, the respondents receive information about available travel options one month ahead of the planned travel. The short-run context concerns temporary changes in available travel options, while the long-run context concerns permanent changes. We find significantly higher valuations of trip attributes in the long-run context. Moreover, our results indicate that the usual arrival time at work as well as the intrinsically preferred arrival time at work serve as reference points in the short-run as well as the long-run choice context, with the former dominating in the short-run context and the latter in the long-run context.

A commuter departure-time model based on cumulative prospect theory

With a focus on planning of departure times during peak hours for commuters, an optimal arrival-time choice is derived using cumulative prospect theory. The model is able to explain the influence of behavioral characteristics on the choice of departure time. First, optimal solutions are derived explicitly for both early and late-arrival prospects. It is shown that the optimal solution is a function of a subjective measure, namely, the gain–loss ratio (GLR), indicating that the actual arrival time of a commuter depends on his or her attitude to the deviation between gains and losses. Some properties of the optimal solution and the GLR are discussed. These properties suggest that the more that the pleasure of gain exceeds the pain of loss, the greater the correlation between actual and preferred arrival times. Finally, a sensitivity analysis of the results is performed, and the use of the model is illustrated with a numerical example based on a skew-normal distribution.

Multiclass, simultaneous route and departure time choice dynamic traffic assignment with an embedded spatial queuing model

ABSTRACTThis paper develops a complementarity formulation for a multi-user class, simultaneous route and departure time choice dynamic user equilibrium (DUE) model. A path-based multiclass cell transmission model (mCTM) is embedded to propagate the traffic flow on the network. Heterogeneous user classes are incorporated in the new formulation and heterogeneity is based on different preferred arrival times, cost perception for travel time, early and late arrival penalties. Multiple model properties have been showed. The proposed model is solved as an equivalent non-monotone variational inequality (VI) problem defined on a product set. A modified proximal point algorithm is used to solve the proposed non-monotone VI problem. Numerical results show that the solution approach is able to find the equilibrium or close to equilibrium solutions. The new formulation and solution approach show the feasibility of solving the multiclass DUE problem for general traffic networks.

Optimal Congestion Pricing with Diverging Long-Run and Short-Run Scheduling Preferences

Recent empirical work has suggested that there is an important distinction between short-run versus long-run scheduling behaviour of commuters, reflected in differences in values of time and schedule delays, as well as in preferred arrival moments, for the short-run versus the long-run problem. Peer et al. (2015) for example find that the average value of time when consumers form their routines in the long-run problem may exceed by a factor 6 the short-run value that governs departure time choice given these routines. For values of schedule delay, in contrast, the short-run value exceeds the long-run value, by a factor 2. And, when forming routines, consumers in fact choose a most preferred arrival time that may deviate from the value they would choose in absence of congestion because a change in routines may mean that shorter delays will be encountered. This paper investigates whether this distinction between short-run and long-run scheduling decisions affect optimal pricing of a congestible facility. Using a stochastic dynamic model of flow congestion for describing short-run equilibria and integrating it with a dynamic model of routine formation, it is found that consistent application of short-run first-best optimal congestion pricing does not optimally decentralize the optimal formation of routines in the long-run problem. A separate instrument, next to road pricing, is therefore needed to optimize routine formation.

Platoon coordination with time windows: an operational perspective

Abstract It has been reported that platooning has the potential of saving fuel and increasing traffic throughput. We formulate a platoon coordination problem with soft time windows as a mixed-integer linear programming problem and solve it with exact solutions. The objective function consists of operation costs, schedule miss penalties and fuel costs. In the numerical example, a Swedish highway network model is used and the computation result shows that, for 21 vehicles, the total cost can be reduced by 3.5% when the optimal preferred arrival times are chosen. A random disturbance is then added to the optimal time windows and the optimal result shows great sensitivity with respect to the disturbance. When the mean of the disturbance becomes larger than 10 minutes, more than half of the platooning benefits will be lost. The study also analyzes the change of different cost compositions as disturbance increases.