Passenger Route Research Articles

Branch and Price for the Stochastic Traveling Salesman Problem with Generalized Latency

We consider an extension of the symmetric traveling salesman problem with generalized latency that explicitly models uncertainty. The stochastic traveling salesman problem with generalized latency (STSP-GL) aims to choose a subset of nodes of an undirected graph and determines a Hamiltonian tour among those nodes, minimizing an objective function that is a weighted combination of route design and passenger routing costs. These nodes are selected to ensure that a predefined percentage of uncertain passenger demand is served with a given probability. We formulate the STSP-GL as a stochastic program and propose a branch-and-price algorithm for solving its deterministic equivalent. We also develop a local search approach with which we improve the performance of the branch-and-price approach. We assess the efficiency of the proposed methods on a set of instances from the literature. We demonstrate that the proposed methods outperform a known benchmark, improving upper bounds by up to 85% and lower bounds by up to 55%. Finally, we show that solutions of the stochastic model are both more cost-effective and robust than those of the deterministic model. History: This paper has been accepted for the Transportation Science Special Issue on TSL Conference 2023. Funding: This work was supported by the Bundesministerium für Bildung und Forschung [Grant 03ZU1105FA]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0417 .

Dynamic Passenger Route Guidance in the Multimodal Transit System With Graph Representation and Attention Based Deep Reinforcement Learning

Dynamic Passenger Route Guidance in the Multimodal Transit System With Graph Representation and Attention Based Deep Reinforcement Learning

Sharing a Ride: A Dual-Service Model of People and Parcels Sharing Taxis with Loose Time Windows of Parcels

(1) Efficient resource utilization in urban transport necessitates the integration of passenger and freight transport systems. Current research focuses on dynamically responding to both passenger and parcel orders, typically by initially planning passenger routes and then dynamically inserting parcel requests. However, this approach overlooks the inherent flexibility in parcel delivery times compared to the stringent time constraints of passenger transport. (2) This study introduces a novel approach to enhance taxi resource utilization by proposing a shared model for people and parcel transport, designated as the SARP-LTW (Sharing a ride problem with loose time windows of parcels) model. Our model accommodates loose time windows for parcel deliveries and initially defines the parcel delivery routes for each taxi before each working day, which was prior to addressing passenger requests. Once the working day of each taxi commences, all taxis will prioritize serving the dynamic passenger travel requests, minimizing the delay for these requests, with the only requirement being to ensure that all pre-scheduled parcels can be delivered to their destinations. (3) This dual-service approach aims to optimize profits while balancing the time-sensitivity of passenger orders against the flexibility in parcel delivery. Furthermore, we improved the adaptive large neighborhood search algorithm by introducing an ant colony information update mechanism (AC-ALNS) to solve the SARP-LTW efficiently. (4) Numerical analysis of the well-known Solomon set of benchmark instances demonstrates that the SARP-LTW model outperforms the SARP model in profit rate, revenue, and revenue stability, with improvements of 48%, 46%, and 49%, respectively. Our proposed approach enables taxi companies to maximize vehicle utilization, reducing idle time and increasing revenue.

Multimodal urban mobility solutions for a smart campus using artificial neural networks for route determination and an algorithm for arrival time prediction

Artificial intelligence algorithms play a key role in solving multimodal urban mobility problems, this study outlines a scheme covering bus, boat, and pedestrian transport modes. This involves designing an Artificial Neural Network (ANN) model to classify the most suitable routes for passengers, together with an algorithm capable of estimating the arrival times of various transportation modes at designated stopping points or buildings within the smart campus. The information used to train the ANN is obtained from an Internet of Things (IoT) network and a database that includes the available routes within the campus (which is situated in the Brazilian Amazon), where there is a multimodal electric mobility service. In seeking to achieve the objectives, this ANN relied on the user's geographic location in the input layer, and route mapping data stored in the database in the output layer, as well as the backpropagation algorithm for computational processing and adjustments of synaptic weight. The algorithm for modal arrival time prediction used real-time geographic location data, together with the Haversine formula to calculate geographic distance, while the average speed was obtained from the IoT network. A range of different ANN parameters were included in the experiments. In the study case, the ANN results show an improvement of above 92% for determining the best routes and predicting arrival times. The findings show that ANNs can effectively find the best route within a real-world smart campus environment. Moreover, the results show the arrival time can be estimated by using real-time geographic location data.

Passenger Route and Departure Time Guidance Under Disruptions in Oversaturated Urban Rail Transit Networks

The urban rail transit (URT) system attracts many commuters with its punctuality and convenience. However, it is vulnerable to disruptions caused by factors such as extreme weather and temporary equipment failures, which greatly affect passengers’ journeys and diminish the system’s service quality. In this study, we propose targeted travel guidance for passengers at different space–time locations by devising passenger rescheduling strategies during disruptions. This guidance not only offers insights into route changes but also provides practical recommendations for delaying departure times when required. We present a novel three-feature four-group passenger classification principle, integrating temporal, spatial, and spatiotemporal features to classify passengers in disrupted URT networks. This approach results in the creation of four distinct solution spaces based on passenger groups. A mixed integer programming model is built based on individual level considering the first-in-first-out rule in oversaturated networks. In addition, we present a two-stage solution approach for handling the complex issues in large-scale networks. Experimental results from both small-scale artificial networks and the real-world Beijing URT network validate the efficacy of our proposed passenger rescheduling strategies in mitigating disruptions. Specifically, when compared to scenarios with no travel guidance during disruptions, our strategies achieve a substantial reduction in total passenger travel times by 29.7% and 50.9%, respectively, underscoring the effectiveness in managing unexpected disruptions.

Considering the Optimization Design of Urban Bus Network Scheduling

At present, the optimization of public transportation networks and vehicle scheduling are carried out independently in stages. However, through analysis, it has been found that scheduling information such as route schedules is an important factor related to passenger route selection. Therefore, in order to further improve the optimization effect, this article proposes an innovative idea of simultaneously optimizing the line network and scheduling. Based on the construction of a real–virtual public transportation network, this article constructs a synchronous optimization model for the line network and scheduling by considering both passenger waiting and on-board time. To achieve the consideration of passengers for different route choices, a shortest path traversal algorithm based on Yen was proposed to analyze the number and weight of the shortest paths between the same OD, and a genetic algorithm was used to solve the model. Finally, the effectiveness of the model was verified through numerical examples, and the results showed that synchronous optimization was superior to phased optimization: the passenger time cost was reduced by 21.5%, the bus operation cost was reduced by 13.7%, and the total bus system cost was reduced by 18.0%.

Behavior-Adaptive Sync-Flow Framework: Integrating frequency setting and passenger routing in oversaturated urban rail transit networks

Urban rail transit (URT) networks serve a large amount of travel demand in metropolitan areas. One of the fundamental challenges in URT demand management is the dynamic interaction between train frequencies and passenger routing behaviors, which becomes even more complicated in oversaturated scenarios. Introducing the behavior-adaptive sync-flow (BASF) framework, this study holistically addresses the tactical frequency settings (train flows) for URT networks and the responsive route choice behaviors of passengers (passenger flows). The proposed integrated model incorporates a schedule-based passenger assignment within space–time networks to model passengers’ routing behaviors based on time-dependent route travel costs. In addition to travel time, the often-overlooked factors such as in-train congestion, number of passengers being left behind, and number of transfers are considered as components influencing travel costs and, consequently, routing behaviors at an individual level. A gradient descent-based heuristic iterative solution approach is developed to address the integrated model by dividing it into three sub-problems. In this iterative process, passengers respond according to a logit-based route choice model based on time-dependent route travel costs, while frequencies are updated using a gradient descent-based strategy. Central to the proposed BASF framework are the mutual adaptations between URT line frequencies and passenger route choices. Numerical examples and a case study of the Beijing URT network highlight the effectiveness and scalability of the model framework in tackling complex, large-scale URT networks, particularly in oversaturated conditions, thus revealing the significance of supply–demand matching.

Railway line planning with passenger routing: Direct-service network representations and a two-phase solution approach

The railway line planning problem (LPP) plays a crucial role in determining the quality of services provided to passengers, as well as operation costs borne by railway companies. In periodic railway LPPs, it is common to consider passenger transfers between train lines to realize a general passenger travel cost setting in the railway system. While detecting passenger transfers requires incorporating passenger routing into mathematical formulations, thereby significantly complicating the problem. Studies on transfer-included LPPs are generally based on the Change&Go network that is constructed based on a pre-given line pool, which however is usually non-exhaustive due to computational intractability. To efficiently include passenger transfers in large-scale railway LPPs, this paper proposes a novel extended direct-service network representation of LPP, where lines are dynamically generated within the optimization process, and part of passenger transfers between lines can be precisely captured without the need for explicit modeling of passengers’ distribution on specific lines. A two-phase solution approach based on the representation is designed. The first phase formulates LPP with part of transfers as a path-based service network design model, solved using a branch-price-and-cut algorithm. The second phase conducts a neighborhood search around the first-phase solution to seek better ones when considering all passenger transfers. Numerical results showcase the good performance of the two-phase solution approach. It delivers optimal solutions in 18 out of 24 test instances for a small case and achieves optimality gaps within 2.85% across all small instances. The large case study of China’s Shandong high-speed railway network whose line pool size reaches millions demonstrates the scalability of the approach and its advantage over the traditional Change&Go method with partial line pools and an exact model developed in the paper.

A Risk Assessment Model for Navigation Safety of Maritime Aquaculture Platform Based on AIS Ship Trajectory

With the rapid increase of maritime aquaculture platform, how to reduce its impact on maritime traffic safety is of great significance to the rapid development of shipping. According to the characteristics of aquaculture platform and maritime environment, The aquaculture platform is superimposed on the ship AIS (Automatic identification systems) big data traffic flow analysis platform. The effects of the distance between the marine aquaculture platform and shipping routes, port facilities and cargo vessel traffic flow channels were studied. The effects between the platform and meteorological conditions, fishing vessel activity and passenger routes were studied. The mathematical assessment model of navigation safety risk of aquaculture platform based on MTLAB was studied. Taking the “Qiandong No. 1” semi-submersible wave energy farming platform as an example. The navigation safety risk assessment is carried out and the risk control measures are put forward. Experiments are carried out with actual data and the effectiveness of the proposed method is proved.

Passenger valuation of interchanges in urban public transport

Understanding how passengers perceive public transport interchanges is important to better explain current public transport mode and route choice behaviour and to better predict future demand levels. In this study we derive how passengers value a public transport interchange in a metropolitan context entirely based on recent, large-scale, Revealed Preference data, explicitly distinguishing between different types and modes of public transport interchanges. For this purpose we estimate three discrete choice models using maximum likelihood estimation, based on over 26,000 passenger route choices observed in June 2023 in the Greater London Area. We find that each public transport interchange is on average valued equivalent to 5 min uncrowded in-vehicle time. Additionally, our model results provide quantitative evidence that cross-platform interchanges between two metro journey legs are valued 20–25 % less negatively than a regular metro interchange where a level change is required. Multimodal bus-metro interchanges and out-of-station interchanges are perceived most negatively by passengers. Passengers value bus-bus interchanges on average about 60 % more negatively than metro-metro interchanges, possibly driven by factors such as comfort, service frequency, reliability and (perceived) safety. Our study results can be used for business case and appraisal purposes, when quantifying the impact of service changes which affect the number or type of interchanges.

Organizing passenger flows at the station complex

This article considers the issue of optimizing passenger traffic at the station complex in modern conditions. The experience and scientific research of foreign scientists from China, Japan and Russia have been studied. Four different ways of organising passengers are discussed, with the most promising and working method revealed to be a comprehensive change in the train schedule and dynamic routing of passengers with absolute blocking. A forecast model in its turn depends on verifiable information, can more or less produce anticipated approaching volume of people, but it appears to be compelling when there are celebrations or understudy occasions. Whereas building a modern stage with a reason of facilitating an exchanging section does relieve the circumstance at the station and security measures considerably. Lastly, the most issue of serving traveler amid the COVID-19 pandemic were taking off strategy from stations and arrangement of travelers to sit separated at a certain distance for shirking of cleansing. In this master’s proposal, the procedures to handle and gauge traveler stream have been regarded, but activities need to be made amid breakdown of trains have not been surveyed.

Network design and vehicle routing problems in road transport systems: Integrating models and algorithms

In this paper, an integrated approach is proposed that incorporates the road network design problem and the vehicle routing problem, which are very often studied separately. This approach can be adopted when the routes to follow in a transport system have to be designed (e.g., for public transit with fixed and variable routes, or for freight distribution) jointly with the network topology and capacity. The goal is to minimise congestion and the impacts on the network from passengers and freight vehicles. The design (control) variables consist of the optimal vehicle routes and optimised road network; they are both discrete (link topology for passenger and freight, routes for passengers and freight vehicles, etc.) and continuous (link capacity in terms of reserved lanes, signal settings, etc.). The problem is modelled in congested transportation networks (e.g., in an urban centre). A heuristic algorithm is adopted to find the best network configuration and the best vehicle routes. The proposed model and the adopted algorithm are applied in a test system to analyse the limits of the methodology, to verify the advantage obtained from the joint method and its applicability. In the test system, the joint procedure gives good results in terms of cost decreases. The reported methods can help public and private stakeholders in making decisions regarding transportation policy, especially in the urban environment.

Understanding Heterogeneous Passenger Route Choice in Municipal Rail Transit with Express and Local Trains: An Empirical Study in Shanghai

The express/local mode of municipal rail transit provides passengers with multiple alternatives to achieve more efficient and superior travel, in contrast to the conventional all-stop operation mode. However, the various route choices (including direct express trains, direct local trains, or transfers) covering different passenger groups pose a significant challenge to passenger flow assignment. To understand route choice behavior, it is crucial to measure the passenger heterogeneity (variability in individual and trip attributes) in order to propose targeted solutions for operation schemes and service planning. This paper proposes a hybrid model by integrating structural equation modeling and the mixed logit model under express/local mode to estimate the impact of passenger heterogeneity on route choice. An empirical study with revealed preference and stated preference surveys carried out in Shanghai revealed how individual and trip attributes quantitatively impact the sensitivity of factors in route choice. The results show that age and trip purpose are more significant factors. Compared to the control group, the probability of express trains is reduced by 10.22% for the elderly and by 11.36% for non-commuters. Our findings can provide support for more reasonable operation schemes and more targeted services.

The Analysis and AI Simulation of Passenger Flows in an Airport Terminal: A Decision-Making Tool

In this paper, a decision-making tool is proposed that can utilize different strategies to deal with passenger flows in airport terminals. A simulation model has been developed to investigate these strategies, which can be updated and modified based on the current requirements of an airport terminal. The proposed tool could help airport managers and relevant decision makers proactively mitigate potential risks and evaluate crowd management strategies. The aim is to eliminate risk factors due to overcrowding and minimize passenger waiting times within the terminal to provide a seamless, safe and satisfying travel experience. Overcrowding in certain areas of the terminal makes it difficult for passengers to move freely and increases the risk of accidents (especially in the event of an emergency), security problems and service interruptions. In addition, long queues can lead to frustration among passengers and increase potential conflicts or stress-related incidents. Based on the derived results, the optimized routing of passengers using modern technological solutions is the most promising crowd management strategy for a sample airport that can handle 800 passengers per hour.

Passenger Routing Algorithm for COVID-19 Spread Prevention by Minimising Overcrowding

COVID-19 has become a pandemic which has resulted in measures being taken for the health and safety of people. The spreading of this disease is particularly evident in indoor spaces, which tend to get overcrowded with people. One such place is the airport where a plethora of passengers gather in common places, such as coffee shops and duty-free shops as well as toilets and gates. Guiding the passengers to less overcrowded places within the airport may be a solution to reduce disease spread. In this paper, we suggest a passenger routing algorithm whereby the passengers are guided to less crowded places by using a weighting factor, which is minimised to accomplish the desired goal. We modeled a number of shops in an airport using the AnyLogic software and we tested the algorithm showing that the exposure time is less with routing and that people are appropriately spread out across the common spaces, thus preventing overcrowding. Finally, we added a real airport in Kavala, Greece to show the efficiency of our approach.

Handling uncertainty in train timetable rescheduling: A review of the literature and future research directions

External and internal factors can cause disturbances or disruptions in daily train operations, leading to deviations from official timetables and passenger delays. As a result, efficient train timetable rescheduling (TTR) methods are necessary to restore disrupted train services. Although TTR has been a popular research topic in recent years, the uncertain characteristics of railways have not been sufficiently addressed. This review first identifies the primary uncertainties of TTR and examines their impacts on both TTR and passenger routing during disturbances or disruptions. It finds that only a few uncertainties have been investigated, and the existing solution methods do not adequately meet practical requirements, such as considering the dynamic nature of disturbances or disruptions, which is crucial for real-world applications. Therefore, the review highlights problems associated with TTR uncertainties that need urgent attention and suggests promising methodologies that could effectively address these issues as future research directions. This review aims to help practitioners develop improved automatic train-dispatching systems with better train-rescheduling performance under disturbances or disruptions compared to current systems.

ОЦЕНКА ЭКОЛОГИЧЕСКОЙ БЕЗОПАСНОСТИ И ЭНЕРГЕТИЧЕСКОЙ ЭФФЕКТИВНОСТИ ТРАНСПОРТНЫХ СРЕДСТВ, ОБСЛУЖИВАЮЩИХ МАРШРУТЫ ГОРОДСКОГО НАЗЕМНОГО ТРАНСПОРТНОГО КОМПЛЕКСА

The urban passenger transport complex is the most important part of the infrastructure of modern cities and provides a number of key functions. The condition for the effective operation of the transport complex is the balanced development of the subsystems included in its structure, which in turn necessitates the formation of a methodological framework that determines the conditions for such development. Based on the indicated provisions, the purpose of the study is formulated, which is to increase the efficiency of the operation of vehicles serving the routes of the urban ground passenger transport complex, based on the results of an assessment of their environmental safety and energy efficiency. To achieve this goal, it is necessary to solve a number of tasks: – study and analysis of the content of research papers, regulatory and technical documents in the field of environmental safety assessment of energy efficiency of vehicles; – development of a methodology for assessing and comparative analysis of environmental safety and energy efficiency, vehicles serving urban public transport routes; – assessment of environmental safety of various categories of vehicles serving the routes of the urban ground passenger transport complex; – generalization and analysis of the results obtained, formulation of conclusions and practical recommendations. During the study, an approach was used that assumes: modeling of energy consumption parameters of vehicles implementing various technologies of fuel and energy supply and a comparative assessment of indicators determining their energy efficiency and environmental safety in traffic conditions on urban passenger routes. Based on the implemented approach, the methods of system and statistical analysis of the initial information, mathematical modeling and methods of multi-criteria evaluation of the results are used in the work. The main result of the study is the numerical values of the indicators of energy efficiency and environmental safety of vehicles implementing various technologies of fuel and energy supply in the conditions of urban ground passenger transport complex, which is the scientific novelty of the study. The use of the obtained values in assessing the efficiency of passenger transportation by vehicles of various categories allows for the formation of a fleet of vehicles based on the results of a multi-criteria analysis. One of the directions of further research involving the use of the results obtained is the development of a comprehensive methodology for determining the optimal structure and coordinated formation of subsystems that make up the urban ground passenger transport complex.

Bilevel Optimization for Traffic Mitigation in Optimal Transport Networks.

Global infrastructure robustness and local transport efficiency are critical requirements for transportation networks. However, since passengers often travel greedily to maximize their own benefit and trigger traffic jams, overall transportation performance can be heavily disrupted. We develop adaptation rules that leverage optimal transport theory to effectively route passengers along their shortest paths while also strategically tuning edge weights to optimize traffic. As a result, we enforce both global and local optimality of transport. We prove the efficacy of our approach on synthetic networks and on real data. Our findings on the international European highways suggest that thoughtfully devised routing schemes might help to lower car-produced carbon emissions.

Модель региональной транспортной сети для построения рациональных мультимодальных маршрутов пассажирских перевозок

Purpose: Adaptation of methods of constructing models of transport networks for the development of rational multimodal routes of regional passenger transportation, as well as the creation of a software product for the practical implementation of the proposed algorithms. Methods: Mathematical modeling; graph theory for solving regional transportation problems with construction of two-level oriented weighted multigraphs; Analysis of existing algorithms for optimizing the search for graph solutions; Dijkstra’s algorithm. Results: The model of the regional passenger transport network has been implemented in the form of a calculation program developed by the authors. In this program, the schedule of the movement of all transport points under consideration is the initial data, and the result is the construction of rational multimodal routes. The calculation program is compiled from several modules that implement the following functionality in general: reading input data from files; the formation of an array of connections for the problems under consideration; search for a rational solution and output of the received data while saving it to the file. The optimization criterion when choosing a route can be the minimum travel time, distance, or cost of the trip, depending on the task and input data provided. The resulting array of data with the results has been further analyzed using the MS Excel table editor. This approach is selected due to sufficient flexibility when performing a variety of data analysis and the convenience of graphic presentation of the results. Practical significance: The development of rational multimodal routes, relevant primarily for cases of abrupt changes in transport relations, in which the extrapolation methods of existing passenger flow do not provide reliable forecast data. Justification of the adjustment of the schedules of regional passenger routes based on the results of the analysis of the demand and the load of their specific segments.

Public transport route choice modelling: Reducing estimation bias when using smart card data

Automated Fare Collection (AFC) data for public transport analyses has received much research interest recently, including its use for the estimation of passenger route choice preferences. However, an important problem persists since AFC data only includes information about the trip within the public transport system, that is stop-to-stop (tap-in to tap-out). Not knowing the full trip from door-to-door might lead to estimation bias, especially when estimating route choice models based on only the chosen stops, which is common practice in current research using AFC data. To avoid this, we propose an improved method for estimating route choice models in public transport using AFC data. The method is based on randomly generating pseudo origin (and destination) points in close vicinity of the actually chosen origin (and destination) stops, thus allowing pseudo access and egress times to be incorporated into the route choice model. The framework is compatible with any probability density function. We suggest using the Beta distribution for generating points when knowledge about access and egress distances are available, whereas the Uniform distribution is suggested when no knowledge is available. The method was applied on replicated AFC data based on traditional travel survey data from the Greater Copenhagen area in Denmark. The results of the model estimations confirm estimation bias in parameter estimates when not correcting for the lack of access/egress information. The proposed method notably improves in-vehicle-time parameter estimates of the route choice model compared to estimation assuming AFC stop-to-stop data, whereas access/egress time and hidden waiting time parameters are still biased, although to a lesser extent than a traditional naïve estimation based on stop-to-stop data.