Trip Scheduling Research Articles

Validation of the Paratransit Skills Assessment for Paratransit Travel and Mobility of Adults on the Autism Spectrum

The Paratransit Skills Assessment (PaSA) was developed in 2011 to assist individuals on the autism spectrum in accessing public paratransit services. Paratransit is a demand-responsive service that often has variable schedules and routes, and may be designated for transportation-disadvantaged populations. It is a criterion-referenced assessment which was designed with three hierarchical sections for the three different paratransit service types as well as a trip planning and scheduling section. It is composed of 37 discrete tasks that are used to make a determination if an individual can use paratransit independently. This paper is on the validation process. The validation process was conducted with 98 study subjects who participated in the PaSA, of which 86 were retested, with 60 retests with non-paratransit users through in-vehicle trips, and 26 retests with current users through interviews about their revealed experiences in using paratransit services. The results indicate that PaSA is able to predict the capabilities of individuals on the autism spectrum to use paratransit services with a 96.4% accuracy and a correlation coefficient of 0.995. The study subjects were found to be non-homogeneous in abilities and skills while the outcomes were found to be highly to moderately reliable. The research findings including the high accuracy rate provide validation that the PaSA is a reliable method for predicting an individual’s ability to use paratransit services independently. However, individuals that were found to only have the six basic skills required for independent travel were recommended to have additional training prior to independent travel. These validation results can be useful for transit providers, transportation experts, and professionals working on issues with transportation-disadvantaged populations.

Vehicle Scheduling of Single-Line Bus Service Using Operational Strategies

Vehicle scheduling is an important component of the transit operations planning process because it is directly related to the number of vehicles required for covering all trips. However, in practice, vehicles are not scheduled by a very efficient approach, related to the fact that all vehicles use one type of operation strategy—full operation strategy—for meeting unbalanced demand patterns. This paper proposes a methodology to reduce the required number of vehicles by adjusting a few trips to operate by other strategies, including limited stop, short turn, deadheading, and mixed strategies. This proposed methodology is based on a graphical human–machine interactive technique, called deficit function (DF). The DF allows schedulers to select a variable trip schedule for reducing the number of vehicles using optimal determination of the best applied strategies. The process of adjusting variable trips to accommodate different strategies may result in passenger travel time changes. Two optimization models are developed to minimize these changes and determine a set of stops served by the variable trip schedule. This proposed methodology has been applied to a detailed example and a case study in Paris, France. The results show that the adjustment of a few trips to use certain strategies can reduce the number of buses required by three.

EXPLOITING THE TIMETABLING FLEXIBILITY IN THE CONTEXT OF THE VEHICLE SCHEDULING PROBLEM WITH HETEROGENEOUS FLEET

ABSTRACT In the context of a heterogeneous fleet, the single-depot Vehicle Scheduling Problem is known as the Vehicle Type Scheduling Problem (VTSP). In this paper, we extend a VTSP model proposed in the literature to enable timetabling flexibility. Two strategies were developed to promote flexibility: aggregated trips and time windows. The former allows to aggregate two or more trips, occurring close in time, into a single one through the use of larger vehicle types, while the latter allows shifting the departure time of scheduled trips backward in few minutes. Both approaches were tested on generated instances that simulate the traffic behavior of a Brazilian city, where clock-face departure pattern is not required and demand peak times occur. We verified significant savings in the daily operation of the public transportation service with acceptable impact on passengers’ experience.

Hybrid Genetic Algorithms and Simulated Annealing for Multi-trip Vehicle Routing Problem with Time Windows

Vehicle routing problem with time windows (VRPTW) is one of NP-hard problem. Multi-trip is approach to solve the VRPTW that looking trip scheduling for gets best result. Even though there are various algorithms for the problem, there is opportunity to improve the existing algorithms in order gaining a better result. In this research, genetic algoritm is hybridized with simulated annealing algoritm to solve the problem. Genetic algoritm is employed to explore global search area and simulated annealing is employed to exploit local search area. Four combination types of genetic algorithm and simulated annealing (GA-SA) are tested to get the best solution. The computational experiment shows that GA-SA1 and GA-SA4 can produced the most optimal fitness average values with each value was 1.0888 and 1.0887. However GA-SA4 can found the best fitness chromosome faster than GA-SA1.

Popular route planning with travel cost estimation from trajectories

With the increasing number of GPS-equipped vehicles, more and more trajectories are generated continuously, based on which some urban applications become feasible, such as route planning. In general, popular route that has been travelled frequently is a good choice, especially for people who are not familiar with the road networks. Moreover, accurate estimation of the travel cost (such as travel time, travel fee and fuel consumption) will benefit a well scheduled trip plan. In this paper, we address this issue by finding the popular route with travel cost estimation. To this end, we design a system consists of three main components. First, we propose a novel structure, called popular traverse graph where each node is a popular location and each edge is a popular route between locations, to summarize historical trajectories without road network information. Second, we propose a self-adaptive method to model the travel cost on each popular route at different time interval, so that each time interval has a stable travel cost. Finally, based on the graph, given a query consists of source, destination and leaving time, we devise an efficient route planning algorithm which considers optimal route concatenation to search the popular route from source to destination at the leaving time with accurate travel cost estimation. Moreover, we conduct comprehensive experiments and implement our system by a mobile App, the results show that our method is both effective and efficient.

On-Line Timetable Rescheduling in a Transit Line

Public transportation systems in metropolitan areas carry a high density of daily traffic, heterogeneously distributed, and exposed to the negative consequences derived from service disruptions. Breakdowns, accidents, strikes, etc., require on-line operation adjustments to address these incidents and thus reduce their side effects, such as passenger extra-waiting times, complaints, potential operational dangers, etc. The Vehicle Rescheduling Problem consists of defining a new schedule for a set of previously scheduled trips, given that one/several trips cannot be carried out. This paper addresses the rescheduling problem in a transit line that has suffered a fleet size reduction (also denoted as Reduced Fleet Rescheduling Problem). We present different modeling possibilities depending on the assumptions that must be included in the modelization and we show that the problem can be rapidly solved using a reformulation that will be proven to have the integrality property. We test our results in a testbed of random instances outperforming previous results in the literature. We also include a real-world case of the commuter trains of Madrid, Spain to illustrate our solutions.

Daily activity and travel sequences of students, faculty and staff at a large Canadian university

ABSTRACTThis paper examines the activity engagement, sequencing and timing of activities for student, faculty and staff commuter groups at the largest university in the Maritime Provinces of Canada. The daily activity patterns of all university community groups are modeled using the classification and regression tree classifier algorithm. The data used for this study are derived from the Environmentally Aware Travel Diary Survey (EnACT) conducted in spring 2016 at Dalhousie University, Nova Scotia. Results show that there are significant differences in activity and travel behavior between university population segments and the general population in the region, and between campus groups. For example, students participate in more recreation activities compared to faculty and staff. They also take more trips to and from campus, and are more flexible in their scheduling of trips. The insights gained from this study will provide helpful information for promoting sustainability across university campuses, and for development of campus-based travel demand management strategies.

Decomposing the price of the cruise product into tourism and transport attributes: Evidence from the Mediterranean market

The price composition of the cruise product is described by a high level of complexity, since it incorporates both tourist and transport dimensions. Despite that research on cruise sector is advancing, the international literature still lacks of studies focusing explicitly on the composition of cruise product's price. Within this concept, this paper decomposes the price of cruise packages into tourism- and transport-driven characteristics and it builds on the established method of hedonic price modeling (HPM), which is applied on data drafted from the scheduled trips of two contemporary cruise lines for the Mediterranean, to measure their contribution in the final formation of the offered price. The overall analysis validates that the contribution of tourism attributes outweighs this of transport attributes, whereas a high proportion of price variability is also attributed to the different marketing targets adopted by companies, even for those belonging to the same class.

MH370 burst frequency offset analysis and implications on descent rate at end of flight

Malaysian Airlines flight MH370 veered off course unexpectedly during a scheduled trip from Kuala Lumpur to Beijing on the 7th of March 2014. MH370 was tracked via military radar into the Malacca Straits and, after disappearing from radar, was subsequently believed to have turned south towards the southern Indian Ocean before crashing approximately 6 hours later. This article discusses specifically the analysis of burst frequency offset (BFO) metadata from the SATCOM messages. It is shown that the BFOs corresponding to the last two SATCOM messages from the plane at 00:19:29Z and 00:19:37Z 8th March 2014 suggest that flight MH370 was rapidly descending and accelerating downwards when message exchange with the ground station ceased.

Real-time multi-depot vehicle type rescheduling problem

The multiple-depot vehicle type rescheduling problem (MDVTRSP) is a dynamic extension of the classic multiple-depot vehicle scheduling problem (MDVSP), where a heterogeneous fleet is considered. The MDVTRSP consists of finding a new schedule given that a severe disruption occurred in previously scheduled trips very quickly, simultaneously minimizing the transportation costs and the deviations from the original plan. Although several mathematical formulations and solution methods have been developed for the robust MDVTRSP, the real time MDVTRSP is still unexplored. In this paper, we introduce a formulation of the problem and develop a heuristic solution method, employing time-space network, truncated column generation, and preprocessing procedures. The solution method has been implemented in several algorithm variants, combining different developed preprocessing methods. Computational experiments on randomly generated instances were performed to evaluate the performance of the developed algorithms. The best solutions concerning efficiency and efficacy were obtained by the variants considering state space reductions to accelerate the convergence process of the column generation. Solutions were obtained very quickly (in less than 150 seconds for large instances, considering up to 2500 trips, eight depots, and one breakdown. The developed heuristics also presented a good behavior for several simultaneous disruptions, solving the problem with a little increase (less than 8.5%, on average) in the required CPU time. A case study using data from a real-life small instance in Brazil also demonstrated the efficiency and efficacy of the approach when compared with manual planning strategies.

Dynamic discrete choice model for railway ticket cancellation and exchange decisions

The increasing use of internet as a major ticket distribution channel has resulted in passengers becoming more strategic to fare policy. This potentially induces passengers to book the ticket well in advance in order to obtain a lower fare ticket, and later adjust their ticket when they are sure about trip scheduling. This is especially true in flexible refund markets where ticket cancellation and exchange behavior has been recognized as having major impacts on revenues. In this paper, we propose an inter-temporal choice model of ticket cancellation and exchange for railway passengers where customers are assumed to be forward looking agents. A dynamic discrete choice model (DDCM) is applied to predict the timing in which ticket exchange or cancellation occurs in response to fare and trip schedule uncertainty. The problem is formulated as an optimal stopping problem, and a two steps look-ahead policy is adopted to approximate the dynamic programming problem. The approach is applied to real ticket reservation data for intercity railway trips. Estimations results indicate that the DDCM provides more intuitive results when compared to multinomial logit (MNL) models. In addition, validation results show that DDCM has better prediction capability than MNL. The approach developed here in the context of exchange and refund policies for railway revenue management can be extended and applied to other industries that operate under flexible refund policies.

Green accessibility: Estimating the environmental costs of network-time prisms for sustainable transportation planning

Accessibility, or the ease to participate in activities and obtain resources in a given environment, is crucial for evaluating transportation systems. Greater accessibility is often achieved by increasing individuals' potential mobility. However, potential mobility, if realized by motorized modes, can also generate negative environmental impacts such as fossil fuel consumption and greenhouse gas (GHG) emissions. While the negative environmental impacts of greater mobility are acknowledged, there has been a lack of research to validate those impacts using empirical data, especially considering variations in individuals' mobility levels. This paper presents a method for estimating the expected environmental costs of accessibility represented by a network-time prism (NTP). A NTP delimits all accessible locations within a network and the available time for an individual to present at each location given a scheduled trip origin and destination, a time budget and the maximum achievable speeds along network edges. Estimating the expected environmental costs of a NTP involves three steps: (1) semi-Markov techniques to simulate the probabilities to move along network edges at given times; (2) the speed profiles for reachable edges, and (3) a cost function that translates speeds into environmental impacts. We focus on air quality and employ the motor vehicle emission simulator MOVESLite to estimate the CO2 emissions at both the edge and prism levels. We calibrate and validate the methods for experimental NTPs defined within the Phoenix, AZ, USA road and highway network using vehicles instrumented with GPS-enabled onboard diagnostic devices (OBD). We demonstrate the effectiveness of our method through two scenarios and investigate the impact of changes in mobility levels on the expected CO2 emissions associated with the experimental NTPs.

Capturing value of reliability through road pricing in congested traffic under uncertainty

Empirical studies showed that travel time reliability, usually measured by travel time variance, is strongly correlated with travel time itself. Travel time is highly volatile when the demand approaches or exceeds the capacity. Travel time variability is associated with the level of congestion, and could represent additional costs for travelers who prefer punctual arrivals. Although many studies propose to use road pricing as a tool to capture the value of travel time (VOT) savings and to induce better road usage patterns, the role of the value of reliability (VOR) in designing road pricing schemes has rarely been studied. By using road pricing as a tool to spread out the peak demand, traffic management agencies could improve the utility of travelers who prefer punctual arrivals under traffic congestion and stochastic network conditions. Therefore, we could capture the value of travel time reliability using road pricing, which is rarely discussed in the literature. To quantify the value of travel time reliability (or reliability improvement), we need to integrate trip scheduling, endogenous traffic congestion, travel time uncertainty, and pricing strategies in one modeling framework. This paper developed such a model to capture the impact of pricing on various costs components that affect travel choices, and the role of travel time reliability in shaping departure patterns, queuing process, and the choice of optimal pricing. The model also shows the benefits of improving travel time reliability in various ways. Findings from this paper could help to expand the scope of road pricing, and to develop more comprehensive travel demand management schemes.

Independent travel recommendation algorithm based on analytical hierarchy process and simulated annealing for professional tourist

Independent travelers, especially professional independent travelers, tend to plan their trip schedules according to their interests, preferred hotels, landmarks they wish to visit, budgets, time availability and various other factors. Hence, travel schedule planning is valuable for satisfying the unique needs of each traveler. In this paper, we propose an algorithm for independent travel recommendation, consisting of three steps. Firstly, landmarks in the destination are selected under the specific constraints, which is modeled as a 0-1 knapsack problem. Then, the landmarks will be evaluated comprehensively using AHP (Analytic Hierarchy Process) model, and the greedy simulated annealing algorithm is adopted to select the best landmarks with high evaluation scores. Next, with AHP-decision model, a most reasonable free line to the tourist destination is selected from multiple candidates. Lastly, the path planning among the landmarks is abstracted as a TSP (Travelling Sales Problem) problem, and the simulated annealing algorithm based on roulette wheel selection is adopted to solve it. Through simulation experiments, by comparing with package tour from the aspects of landmark selection, valid sightseeing time ratio, valid sightseeing consumption ratio and the tourist satisfaction, the proposed algorithm is evaluated and analyzed. Simulation results illustrate the feasibility and rationality of our approach, which can be used as an effective reference deciding individualized travel schedules and trip planning.

An exact decomposition method to save trips in cooperative pickup and delivery based on scheduled trips and profit distribution

Compared to the non-cooperative mode, the cooperative mode is a powerful way to reduce operational cost in pickup and delivery service. In order to protect business sensitive information, sometimes participants are unwilling to open the customer's detailed information. Thus, we utilize the publishable trip scheduled results to compute the saved trips brought by cooperation. A mathematical model minimizing trips of cooperation is proposed. To obtain the exact solution, we define the cooperative trip set. We prove that only when cooperative trip set exists it is possible to save trips by cooperation. For a two-trip cooperative trip set, we exactly obtain the saved trips by enumerating all feasible cooperative cases. For a K-trip cooperative trip set, we propose an exact method to obtain the saved trips by decomposing it to at most K-1 two-trip cooperative trip sets. Computational complexity of the based-on-decomposition exact algorithm is O(N), where N is the total number of trips. Using the based-on-decomposition algorithm, we calculate the exact Shapley value to distribute profit. To empirically verify the exact method, we perform the extensive experiment cases of the real cooperative pickup and delivery service, i.e., “picking up and delivering customers to airport service” (PDCA).

Analytical equilibrium of bicriterion choices with heterogeneous user preferences: application to the morning commute problem

ABSTRACTThe morning commute problem, introduced in Vickrey [1969. “Congestion Theory and Transport Investment.” American Economic Review 56: 251–260], addresses the equilibrium trip schedule of the users for commuting through a single bottleneck with fixed capacity over the morning peak. In this paper, we adapt the concept of the efficient frontier from Portfolio Theory [Markowitz, H. 1952. “Portfolio Selection.” The Journal of Finance 7 (1): 77–91] to propose an analytical solution to the morning commute problem with a general distribution of the schedule preferences over time and a continuous joint distribution of schedule penalty preferences over the population of the commuters. On this basis, we analytically derive the equilibrium arrivals of the heterogeneous commuters to the bottleneck given the independent probability distributions of earliness and lateness penalty factors. We also propose a method to retrieve independent probability distributions of the schedule penalty factors in the equilibrium condition from a joint distribution. The proposed model can be inversely used to approximate the independent distribution of schedule penalty preferences of the user for the early and late commuters using empirical arrival time data from the network. The result is also used to propose a dynamic pricing strategy to optimize the system by avoiding the formation of a queue, which can also be extended to design a dynamic pricing strategy on the network level using the macroscopic fundamental diagram (MFD). To provide an example, the approach is employed to derive a closed-form solution when the probability distribution of the preferences is uniform. A numerical example is also presented using the proposed model to compare the solutions for different distributions of the schedule deviation penalty preferences.

Capturing Value of Reliability through Road Pricing in Congested Traffic under Uncertainty

Abstract Empirical studies showed that travel time reliability, usually measured by travel time variance, is strongly correlated with travel time itself. Travel time is highly volatile when the demand approaches or exceeds the capacity. Travel time variability is associated with the level of congestion, and could represent additional costs for travelers who prefer punctual arrivals. Although many studies propose to use road pricing as a tool to capture the value of travel time (VOT) savings and to induce better road usage patterns, the role of the value of reliability (VOR) in designing road pricing schemes has rarely been studied. By using road pricing as a tool to spread out the peak demand, traffic management agencies could improve the utility of travelers who prefer punctual arrivals under traffic congestion and stochastic network conditions. Therefore, we could capture the value of travel time reliability using road pricing, which is rarely discussed in the literature. To quantify the value of travel time reliability (or reliability improvement), we need to integrate trip scheduling, endogenous traffic congestion, travel time uncertainty, and pricing strategies in one modeling framework. This paper developed such a model to capture the impact of pricing on various costs components that affect travel choices, and the role of travel time reliability in shaping departure patterns, queuing process, and the choice of optimal pricing. The model also shows the benefits of improving travel time reliability in various ways. Findings from this paper could help to expand the scope of road pricing, and to develop more comprehensive travel demand management schemes.

Evaluation of an integrated mobile payment, route planner and social network solution for public transport

Abstract The proliferation of new technologies is revolutionizing the public transport sector, allowing Operators to replace complex and expensive infrastructures by travellers’ mobile devices and online management channels and platforms. This paper aims to present the Seamless Mobility platform, a disruptive solution based on these new channels, which main goal is to facilitate and promote public transport usage for travellers, as well as reducing operational costs for public transport companies. The Seamless Mobility platform integrates three main components: (i) mobile payments, (ii) route planner, and (iii) social network. The payment component is based on the pay-as-you-go concept with check-in and check-out requiring the reading of the corresponding QR Code station. The route planner combines information from published schedules with real-time information to identify the nearest stops, the next departures, or the best route for a scheduled trip. The social network component allows real time sharing among travellers of public transport information, related to several aspects of the service (e.g. noise, skilfulness of drivers). To test the concept, a mobile application, called OneRide, was developed. This application was tested by users in real environment, in the city of Porto, Portugal. The results show that users considered the system extremely useful, since it is more convenient than traditional systems. It was also clear that users valued the integration of additional and complementary services with mobile payments, such as information about their journey, maps and schedules. Regarding the social component some users found it difficult to understand the concept, but once they understood they considered it very useful. The use of the QR Codes to perform the payment has shown to be one of the main challenges to be addressed, since lighting conditions, position and distance to the QR Code influences the reading process.

Urban commuters’ valuation of travel time reliability based on stated preference survey: A case study of Beijing

Regarding travel mode choices, urban commuters always consider not only travel time but also travel time reliability of different modes. In this study, schedule delays were used for measuring travel reliability. The trip scheduling model and binary logit model were used to estimate the value of travel time reliability. First, by considering the impacts of the income level and time constraint, the trip scheduling models were developed. Then, data for model calibration was collected through street-interview survey based on the stated preference (SP) approach. Finally, the model was calibrated using the collected data and the commuters’ value of travel time reliability were estimated. On the basis of the model results, the influence of income level and time constraint on the value of travel time reliability was analyzed. The results revealed that the value of travel time reliability differed significantly for different income and time constraint levels, and transportation modes. The developed models for travel time reliability analysis can be applied to supporting the travel cost analysis in the future.

Driving strategy optimization for trains in subway systems

Energy-efficient operation of trains in subway systems is regarded as one of the most effective strategies to cut down the operational cost. Most of the studies about the energy-efficient operation of trains aim to minimize the consumption of mechanical energy of trains. In this paper, an energy-efficient train control model is proposed under the practical billing system by introducing the traction efficiency. In addition, a numerical solution approach is presented to solve the energy-efficient driving strategy, which consists of the control sequences and the corresponding switching points. First, the authors analyze the energy-efficient control problem by applying the maximum principle, from which the energy-efficient control regime is proved to include the maximum acceleration, coasting, cruising with partial braking, and maximum braking. Second, the control sequences for one section are fully analyzed and an algorithm is presented to calculate the switching points with the energy consumption constraint. To obtain the solution to an interval with variable speed limits and gradients, the interval is divided into several sections, where the gradient and speed limit of each section are constants. A primary energy-efficient solution, which costs a longer trip time, is given. Then, energy units are assigned into multiple sections to shorten the trip time of the primary solution. Specifically, each energy unit is added into the selected section to achieve the maximum time reduction. Finally, the energy-efficient driving strategy at all sections will be obtained when the scheduled trip time of the interval is delivered. Some examples based on the practical data are conducted to illustrate that the proposed approach has a good potential on saving energy of trains.