Passenger Travel Time Research Articles

Multi-constrained bus holding control in time windows with branch and bound and alternating minimization

This work proposes a periodic bus holding control method where the bus holding times of all running trips are computed simultaneously within each optimization time period; thus, increasing the coordination among running buses for avoiding bus bunching. This paper considers the adverse effects of the bus holding control in the in-vehicle travel times of on-board passengers and performs holistic bus holding decisions by modelling the bus holding problem as a discrete, nonlinear, constrained optimization problem. Given the computational complexity of the bus holding problem, an alternating minimization approach is introduced for computing the optimal holding times at each optimization instance. The performance of the periodic control method is evaluated against the performance of event-based control methods using 5-month automated vehicle location and automated passenger count data from bus line 1 in Stockholm for contacting simulation-based experiments.

Data-driven model for passenger route choice in urban metro network

Passenger flow distribution in the metro system is fundamental for many applications such as network planning and design, passenger flow forecasting, individual travel activity modeling and emergency response management. However, in most metro systems the smart card automated fare collection (AFC) equipment in Beijing only record when and where a passenger enters and leaves the metro network. Therefore, how to accurately determine passenger flow distribution in unknown travel routes remains a challenging task for the managers. This paper presents a methodology for reconstructing metro passenger flow distribution from large-scale smart card data. A clustering method was first applied to group the travel time of passengers between origin–destination (OD) station pairs into different clusters. Then an approach was proposed that considered both uncertain walking time and transfer time, to estimate the theoretical travel time of all possible routes between the OD pair. An approach to measure the similarity was further employed to match each travel time cluster to a most-likely travel route, and finally obtained the passengers’ flow of every route. Compared with two classical methods, the proposed approach was more accurate and efficient.

Autonomous shuttle bus service timetabling and vehicle scheduling using skip-stop tactic

The development of new technologies that allow us to know more precisely real-time public transport (PT) passenger demand enables the introduction of new methodologies to improve autonomous shuttle bus service (ASBS) from the perspective of both the user and the operator. This work develops a new methodology to determine the optimized PT timetable integrated with vehicle scheduling, including the possibility of using the skip-stop tactic based on real-time passenger demand. The consideration of a skip-stop operation allows for a reduction in the passengers’ total travel time and in the number of autonomous shuttle vehicles in use. The analysis is based on the deficit function (DF) graphical concept and considers the constraints of the vehicle with regard to capacity. The methodology is developed for a single-depot bidirectional circle ASBS route using the DF as a multi-decision choice model. Because of the complexity involved in large-size genetic algorithm problems, a binary-variable iteration method is applied to attain efficient solutions. A case study in Auckland, New Zealand, is employed to assess the new model. The case study shows a reduction of 1.83% in total passenger travel time and 8.11% in the number of vehicles as compared to solutions that do not consider the skip-stop tactic. Moreover, it is shown that the advanced DF-based methodology could be used to develop fully autonomous PT in a network in the future.

Optimizing the release of passenger flow guidance information in urban rail transit network via agent-based simulation

• A agent-based simulation optimization model is proposed to optimize the release of passenger guidance information in a rail network. • A hybrid heuristic solution algorithm , integrated with passenger simulator and genetic algorithm , is developed. • A case study of Beijing subway is carried out with the large-scale smart card data. The passenger flow guidance is an effective demand management strategy to alleviate the excessive congestion in the urban rail transit network. In order to determine the scope and the timing, a simulation-based optimization model is proposed to optimize the release of passenger flow guidance information in the rail transit network in this paper. In the optimization model, we mainly focus on three aspects namely; where, when and what type of the guidance information should be released to the passengers. In the simulation model, the passenger choice behavior is captured by the agent-based simulation method, which responses to the congestion and the guidance information. Based on this, the dynamic passenger flow distribution can be derived. Furthermore, the adoption rate of the displayed guidance information on passenger information system as well as its impact on passenger travel behavior are also considered in the model. A hybrid heuristic solution algorithm, integrated with passenger simulator and genetic algorithm, is developed to solve the proposed simulation-based optimization model. Finally, a case study of Beijing subway is carried out with the large-scale smart card data. The numerical study shows that the passenger flow demand affects the guidance effect significantly and the best guidance effect can be met with sufficiently high passenger flow demand. And the guidance rate is also found to affect the guidance results. The results also show that the proposed model can provide a detailed guidance scheme for every station at selected time intervals. The results show that the dynamic releasing scheme can save up to a total of 46,319 min in passenger travel time during a single guidance period.

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.

A new design and evaluation approach for managed lanes from a sustainability perspective

This article presents a comprehensive framework that optimizes traffic management measures to reduce emissions and fuel consumption and evaluates their operating and secondary environmental impacts. A new managed lane strategy is presented that minimizes the total passenger travel time on the freeway. This managed lane focuses on simultaneously optimizing toll schemes on the high occupancy toll (HOT) lane and the operation of the general purpose (GP) lanes. With reduced congestion and thus reduced number of acceleration and deceleration events associated with stop-and-go traffic, fuel efficiency increases and emissions are reduced. PARAMICS microscopic traffic simulator, which considers the behavior of individual vehicles (e.g., acceleration, deceleration, and lane changing behavior) is used to collect traffic performance and emission data for estimating mobility and emissions measures (i.e., operating phase). While previous studies only focused on operating phase impact, this study uses a Leontief input-output (I-O) model to establish the financial flow between industries to capture the large-scale environmental impacts of HOT lane deployment (i.e., secondary impact). The core of the new evaluation approach lies in its capability to provide a more thorough assessment of the environmental impacts of traffic management schemes by quantifying the impacts associated with the interplay between the activities of various sectors and the transportation industry. The I-O model is utilized to assess the indirect impacts of induced demand generated from network improvements and evaluate the environmental impacts of HOT lane deployment in regional economies. The developed approach is applied to The City of Calgary. The results of the study show that the traditional approaches that only evaluate the operating phase impacts of transportation strategies considerably overestimate the reduction of greenhouse gas (GHG) emissions.

A multi-objective meta-heuristic approach for transit network design and frequency setting problem in a bus transit system

This paper studies a multi-objective transit network design and frequency setting (TNDFS) problem with the aim of determining a set of routes and frequency on each route for public buses. The TNDFS problem is solved in two stages. First, the transit network design problem is solved to generate a set of routes using an initial route set generation (IRSG) procedure combined with genetic algorithm. Second, the frequency setting problem is formulated as a multi-objective model to assign the frequency on each route with the objectives of minimizing the passenger time and the operating cost simultaneously. This problem is solved using the multi-objective particle swarm optimization with multiple search strategies (MMOPSO) to generate a Pareto-Front between the passenger time and the operating cost. An extensive computational experiment is performed on the benchmark Mandl’s Swiss network (Mandl, 1980) considering different scenarios and instances. The results obtained using the proposed approach to solve the transit network design problem are compared with that of the state-of-the-art methodologies available in the literature (Arbex & Cunha, 2015; Kechagiopoulos & Beligiannis, 2014; Nikolić & Teodorović, 2013), which outperforms in terms of trip directness and average travel time of passengers. For the frequency setting problem, the solutions obtained using MMOPSO are justified over the NSGA-II.

Multi-agent simulation for planning and designing new shared mobility services

Limiting private cars' use while promoting sustainable modes of transport is one of the main challenges of urban transport planning. In this context, characterized by scarce resources and increasing demand for mobility, Demand Responsive Shared Transport (DRST) services can bridge the gap between shared low-quality public transport and unsustainable individual private transport. Taking advantage of Information and Communication Technologies (ICT), they can supply transport solutions ranging from flexible transit to ride sharing services, providing real-time “on demand” mobility through fleets of vehicles shared by different passengers. The optimal design of a DRST service requires a trade-off among efficiency (from the operators' point of view), service quality (from the users' point of view) and sustainability (from the community's point of view). In this paper, an agent-based model (ABM) fed with GIS data is used to explore different system configurations of a specific type of DRST service, i.e. flexible transit, and to estimate the transport demand and supply variables that make the service feasible and convenient. The model reproduces a mixed fixed/flexible route transit service with different fleet size and vehicle capacity in the city of Ragusa (Italy) with the aim to: (i) make a first test of the ABM model with GIS-based demand and road network models; (ii) explore different vehicle dispatching strategies; (iii) find appropriate indicators to monitor the service quality and efficiency. Simulation results show the impact of fleet composition and route choice strategy on the system performance. In particular, they show an optimal range of operating vehicles that minimizes a total unit cost indicator, accounting both for passenger travel time and vehicle operation cost. By reproducing the microinteraction between demand and supply agents (i.e. passengers and vehicles), it is possible to monitor the macroscopic behaviour of the system, and derive useful suggestions for the correct planning, management and optimization of DRST services.

Case Study - Benefits from Constructing a Grade-Separated Interchange

The paper presents the results of analyzes regarding the benefits of the planned road network development, reconstruction of a road intersection of the national road No. 25 and provincial road No. 254. Actual and forecasted traffic volumes and saturation factors on the road network were calculated based on the regional simulation transport demand model. The value of transport work performed by all vehicles traveling on this network, the value of travel time of drivers and passengers traveling during the weekday as well as the costs of impact of traffic into the environment were also determined. On this basis, a preliminary economic analysis was made as the base for a feasibility study, to proof economic benefits of such investment. Traffic analyzes, the results of which were presented in this paper, were made using the actual and prognostic four-stage regional simulation transport demand models. The authors adopted a typical trumpet-type of grade separated interchange for economic analyzes. It is a collision-free interchange, and therefore very safe in its very nature. The experience of current road practice indicates that significant improvement of road traffic safety will be the undoubted benefit resulting from the construction of such interchange. Additionally, such construction will also reduce time lost by road users in traffics. It should be noted that at present a railway at-grade crossing located in a short distance from the abovementioned intersections is the most serious source of time lost in traffic by road users. The railway line with heavy passenger and freight traffic causes each closing of the turnpikes effectively blocks traffic on main turns of above mentioned intersection. The construction of the grade separated interchange will enable collision-free intersection of the road with the railway, and thus will reduce the loss of time - a significant improvement in the functioning of the analyzed area.

Estimation of passenger route choices for urban rail transit system based on automatic fare collection mined data

When multiple routes connect the same origin and destination (OD), passenger route choices are related to the operation and management of urban rail transit, including the design of the train plan, fare clearing, and passenger distribution. The passengers’ travel data from an automatic fare collection (AFC) system provides useful data for the analysis of passenger route choices. We propose an estimation method of passenger route choices based on AFC data, which includes passenger’s travel time and OD. In this paper, the AFC data used for analysis were mined to eliminate unreasonable individual travel data. In this study, the components of passenger travel times and their influencing factors are analyzed and the results indicate that the travel times are related to the route choices of passengers. Cluster analysis indicated that passenger travel times follow the logarithmic normal distribution. Based on this and a multi-route distribution sample, an estimation model for passenger route choice proportions is proposed. Furthermore, experimental results show that the data mining method has high accuracy and the result also supported the hypothesis about the travel time distribution. The model was applied to the Chengdu metro, which is one of the largest metro systems in the world, and the obtained results indicate that the model works well. In addition to the initial application, the estimation method provides a new method to calculate the route choice proportion for urban rail transit with maximum dependability and timeliness.

Passenger Travel Time Reliability for Multimodal Public Transport Journeys

Urban transit networks typically consist of multiple modes and the journeys may involve a transfer within or across modes. Therefore, the passenger experience of travel time reliability is based on the whole journey experience including the transfers. Although the impact of transfers on reliability has been highlighted in the literature, the existing indicators either focus on unimodal transfers only or fail to include all components of travel time in reliability measurement. This study extends the existing “reliability buffer time” metric to transit journeys with multimodal transfers and develops a methodology to calculate it using a combination of smartcard and automatic vehicle location data. The developed methodology is applied to a real-life case study for the Amsterdam transit network consisting of bus, metro, and tram lines. By using a consistent method for all journeys in the network, reliability can be compared between different transit modes or between multiple routes for the same origin–destination pair. The developed metric can be used to study the reliability impacts of policies affecting multiple transit modes. It can also be used as an input to behavioral models such as mode, route, or departure time choice models.

High-Speed Rail as a New Mode of Intercity Passenger Transportation

High-speed rail is a new mode of intercity passenger transportation. We review the history of high-speed rail development of the United States (US) and compare it with that of peer countries. With the rapid progress of HSR and the successful competition with cars and air travel between medium and long distances (150 and 1,200 km), HSR has an increasing role in intercity travel worldwide. Decision makers, transportation planners, system designers, and operators as well as political leaders need to understand HSR’s operational boundaries for intercity travel to determine which HSR will outperform the others and under which conditions. Our analysis uses a simple time-distance factor to clarify the dominance. To confirm the validity of HSR in intercity passenger rail services, a comparison with the external competition of car and air travel is necessary. Meanwhile, an internal examination of operational performance considering sophisticated variables is imperative. Based on numerous HSR projects, we select four interrelated trade-off elements: the passenger access time and travel time associated with the total on-line travel time, the area coverage associated with the station density, the station density associated with speed, and the transit unit size, frequency, and loading factor associated with the independent line capacity. After examining the interrelations and trade-offs, we present a practical case study of one of the major US economic corridors — the Northeast Corridor. The case study explores the geospatial metadata and concludes that three major system efficiency challenges exist; therefore, it provides corresponding engineering measures to convert an independent dead-end terminal into an integrated through-running station, which are the priority for converting Amtrak, the US national rail service, into an accelerated HSR service. It is time to renew the government’s interest in paying systematic attention to the comprehensive effect of HSR.

A stochastic congested strategy-based transit assignment model with hard capacity constraints

Abstract This paper expands on the stochastic version of the strategy-based congested transit assignment problem described in Codina and Rosell (2017b), specifically by extending it to cases dealing with explicit line capacities. Following this previous work, the stochastic model with explicit line capacities takes into account the stochastic mean waiting times and in-vehicle travel times of passengers at stops during the standard scenario journey of the planning period. This slightly modified model is formulated also as a stochastic variational inequality derived from the deterministic version of the problem with similar properties and characteristics, formulated by the authors in a previous work. For this stochastic capacitated model, two algorithms are presented which may take advantage of simplified simulation models for the bus stops in order to provide realistic mean waiting times for passengers at stops. Both algorithms can be easily adapted to the case of the model with non-strict capacities by Codina and Rosell (2017b).

Train Scheduling for Minimizing the Total Travel Time With a Skip-Stop Operation in Urban Rail Transit

The skip-stop operating mode for urban rail transit systems defines three types of stations: A, B, and AB stations. In this strategy, all the trains stop at AB stations, and A (B) trains stop at only A (B) stations. Skipping some stops can increase the operating speed, which leads to direct benefits by reducing the in-vehicle time for passengers and improving the operational efficiency for operators. With the skip-stop mode, passengers have to transfer at an AB station when the trains they take do not stop at their destination, and the extra transfer waiting time depends on the AB station distribution and the corresponding headways. In this paper, we develop a train scheduling optimization model to minimize the total passenger travel time so that passengers can get to their destinations faster without experiencing excessive transfer waiting times. A two-phase approach based on a genetic algorithm is developed to solve this mixed-integer nonlinear problem. Finally, the proposed train scheduling optimization model is applied to an idealized corridor and a real-world scenario based on Line 6 of the Beijing URT network. An analysis of the type of scenarios in which skip-stop operations are applicable and the efficiency of different types of trips is presented. The results show that a short headway (below 5–6 min) is favorable for a skip-stop operation and there needs to be at least one AB station for every five stations. The average travel time per passenger can be reduced by more than 1 min for the Beijing scenario under the optimized skip-stop operation.

Integrated Optimization of Train Stop Planning and Scheduling on Metro Lines With Express/Local Mode

Train stopping patterns and schedules play critical roles in the service design of metro lines with express/local mode. Previous studies on express/local mode generally handle the design of train stopping patterns and schedules independently, which cannot ensure the overall optimality of service provision. In this paper, a mixed integer nonlinear programming model is developed to collaboratively adjust train stopping patterns and schedules in order to minimize passenger travel time in express/local mode. With two types of train services provided, overtaking is allowed and a diverse set of passenger route choices is also incorporated into the proposed model. Linearization techniques are applied to transform this model from nonlinear to linear, which enables the proposed model to be handled by commercial linear solvers. To enhance computing efficiency in large-scale problems, a guided branch-and-cut algorithm is designed. The numerical examples on a test line and a real-world metro line are implemented to demonstrate the effectiveness of the proposed model and approach. The proposed approach is compared to existing approaches to identify the benefits of integrating train stop planning and scheduling decisions. Based on the computational time and the objective value, the guided branch-and-cut algorithm outperforms the direct use of the CPLEX solver.

Method for Medium-speed Maglev Train Timetabling with Consideration of Passenger Demands

Medium-speed maglev train serves for suburban passenger flow, and it’s important to research on medium-speed maglev train timetabling with consideration of passenger demands, but there is still no research in this area. Combing the passenger flow scenes for medium-speed maglev traffic and the occupancy characteristics of operation resources, a study on the timetabling of medium-speed maglev train considering passenger demands is conducted in this paper. The timetabling problem is formulated as a 0-1 integer programming model based on cumulative flow variables with the objective of minimizing the total travel time for passengers, and the Lagrange relaxation algorithm is used to solve the model. Finally, the model and algorithm are analyzed and verified by a numerical experiment.

Multiline holding based control for lines merging to a shared transit corridor

ABSTRACTIn transit corridors, multiple lines share a sequence of consecutive stops to provide higher joint frequency in higher demand areas. A key challenge is to coordinate the transition from single line to joint operation. A holding control strategy aimed at minimizing passenger travel times is introduced for lines merging into a shared corridor, accounting for the coordination of vehicle arrivals from the merging lines as well as the regularity of each line. The criterion is tested using an artificial network and a real-world network to analyze the impact of demand distribution and compare cooperative versus single line control. We illustrate how the real-time strategy yields overall passenger gains, depending on the composition of different user groups. Results are assessed based on operation and passenger performance indicators and show that coordination is achieved. When combined with joint control in the common part, the proposed approach achieves consistent network-wide travel time benefits.

Bus Load Inference and Crowding Performance Evaluation through Disaggregate Analysis of Fare Transaction, Vehicle Location, and Passenger Count Data

Comfort is an important aspect of the transit passenger experience. Crowding can significantly decrease passenger comfort and disrupt service delivery, causing passenger travel times to increase and even resulting in passengers being unable to board an arriving vehicle. This research explores the use of automatically collected vehicle location data, fare transaction data, and passenger origin–destination inference to measure crowding on buses. Three model components are involved: scaling vehicle trip-level origin–destination transfer data, measuring crowding as perceived by passengers through performance measures defined for this purpose, and determining the sources of crowding. The latter is important to identify the most effective means of addressing crowding in each case. The models are tested on data from the Massachusetts Bay Transportation Authority, and examples of graphical applications already being used by planners are presented.

Impact of a New Metro Line: Analysis of Metro Passenger Flow and Travel Time Based on Smart Card Data

Over the past few decades, massive volumes of smart card data from metro systems have been used to investigate passengers’ mobility patterns and assess the performance of metro network. With the rapid development of urban rail transit in densely populated areas, new metro lines are constantly designed and operated in recent years. The appearance of new metro lines may significantly affect passenger flow and travel time in the metro network. In this study, smart card data of metro system from Nanjing, China, are used to study the changes of metro passenger flow and travel time due to the operation of a new metro line (i.e., Line 4, opened on 18 January 2017). The impact of the new metro line on passenger flow distribution and travel time in the metro network is first analysed. As commuting is one of the major purposes of metro trips, the impact of the new metro line on commuters’ trips is then explicitly investigated. The results show that the new metro line influences passenger flow, travel time, and travel time reliability in the metro network and has different impacts on different categories of commuters.

Express/local train plan optimization for urban rail transit in condition of full-length and short-turn modes

Operating the express/local train plan can meet the characteristics of imbalance of passenger volume temporal and spatial distribution, and satisfy the demands of long-distance commuters who want to travel fast. Although the program can improve the organizational operating efficiency and passenger service level, it will reduce the carrying capacity of system to some extent. Above all, based on analysing the factors of the express/local train plan and the composition of passenger travel time in detail, the 0-1 integer programming model of stopschedule plan problem was established by minimizing the passenger travel time. And combined with a simulation case, the immune genetic algorithm was designed to study the effect of line capacity.