Transit Route Design Research Articles

Path planning for EVs based on RA-RRT* model

The optimal path planning for EVs (electric vehicles) has gained great attention during the last decade due to the zero pollution emission characteristics and limited power capacity of EV batteries. In this paper, an optimal route search is proposed considering multiple charging stations in a dynamic urban environment, while it is still applicable when the initial available amount of the battery fails to cover a certain travel range. The TRDP (transit route design problem) and TNDP (transit node design problem) are used to search for the most feasible routes based on time and driving range via the improved route-assisted rapid random tree (RA-RRT*) algorithm. Considering the status of charge of an EV’s battery during optimal routes search, three states are investigated between the destination and the aggregators: (1) bypassing the aggregators, (2) stopping over a single aggregator, and (3) stopping over multiple aggregators. During the states (2) and (3), it is required that the EVs be charged at the charging stations obtained by the RA-RRT* algorithm while approaching the destination. The proposed algorithm is tested on a random dataset under certain conditions, that is, traffic flow with congestion and assigned target locations from a given map data, with comparison experiments for efficacy verification.

Feasibility Study of Transferring Shared Bicycle Users with Commuting Demand to Flex-Route Transit—A Case Study of Nanjing City, China

Shared bicycle users with commuting purposes generally need to take a traditional public transit and then use the shared bicycle to complete the first/last mile transport. While shared bicycle provides convenient travel for travelers, it also brings a series of problems such as disorderly parking and road occupancy. Therefore, exploring the problem of travel mode shift between shared bicycle and public transit is of significance for improving the traffic environment and increasing the sharing rate of public transit. This paper introduces the flex-route transit system and quantitatively analyzes the rationality and feasibility of using flex-route transit to pick up and drop off shared bicycle users with commuting demand from the temporal perspectives. A flex-route transit route design model is established with the objective of minimizing the sum of vehicle driving time cost and passenger time cost, and the time cost models of the shared bicycle commuting system and the flex-route transit system are constructed, compared, and analyzed to explore the feasibility of flex-route transit picking up or dropping off shared bicycle users under different conditions. Through the subsequent sensitivity analysis, the influence of passenger demand density, fixed station spacing, and travel preference attributed to the two systems are analyzed separately. The results demonstrate that the flex-route transit can efficiently complete the picking up or dropping off for shared bicycle users under certain conditions.

Dedicated bus lane network design under demand diversion and dynamic traffic congestion: An aggregated network and continuous approximation model approach

This paper proposes an integrated methodological framework to design a spatially heterogeneous bus route network and time-dependent service headways to serve travel demand that varies over time and space. Travelers choose to use either the transit mode or the driving mode (as well as travel paths in the corresponding modal network) that minimizes their equilibrium travel cost. In addition, transit routes involve dedicated bus lanes that occupy part of the city streets and affect the capacity reserved for private cars. Hence, roadway congestion depends on the transit route design, and its dynamic evolution is described by regional macroscopic fundamental diagrams. The proposed modeling framework consists of two parts: a bus network optimization module based on continuum approximation that produces optimal headways and local route spacing, and a dynamic aggregated network model that determines route choice, mode split, and user equilibrium conditions. An iterative solution algorithm is developed to solve the integrated model. Numerical experiments are used to demonstrate the applicability of the proposed modeling framework, and to conduct a careful analysis on the influence of the demand pattern on the transit network design, roadway congestion, and the overall system performance.

Integrating underground line design with existing public transportation systems to increase transit network connectivity: Case study in Greater Cairo

Connectivity is a significant problem in large-scale transit networks because the number of transfers required to conduct a trip is considered a discomfort by transit users. This paper presents a practical solution for an underground metro line planning problem by integrating existing bus and metro networks into a single connected transit network. The proposed method aims to obviate the usual combinatorial complexity when solving a transit route design problem. It aims to increase the overall transit system connectivity by selecting a consistent and non-demand-oriented criterion for the design. The metro lines are designed by minimizing passenger transfers through the transit network according to predefined demand node pairs. The design scheme offers a set of ring route alternatives for a sizeable case study in Greater Cairo. The case study selected sixteen traffic analysis zones, an existing metro network consisting of three main lines (113.6 km long), and twelve main bus lines (487.7 km long) for analysis. TransCAD software was used as the basis for coordinating the stations and lines of both the bus and metro systems. Subsequently, a passenger transfer counting algorithm was implemented to determine the number of transfers required between stations from each origin to each destination. A passenger origin–destination transfer matrix was created using the NetBeans integrated development environment to help determine the number of transfers required to complete trips on the transit network before and after proposing the new line. Based on the evaluation, the ring lines were highly efficient at significantly decreasing passenger transfers between stations with the minimum construction cost. This study will be of value during the strategic stages of the transit line design and will assist in rapidly generating initial solutions when certain demand information is unavailable.

Contextual Bandit-Based Sequential Transit Route Design under Demand Uncertainty

While public transit network design has a wide literature, the study of line planning and route generation under uncertainty is not so well covered. Such uncertainty is present in planning for emerging transit technologies or operating models in which demand data is largely unavailable to make predictions on. In such circumstances, this paper proposes a sequential route generation process in which an operator periodically expands the route set and receives ridership feedback. Using this sensor loop, a reinforcement learning-based route generation methodology is proposed to support line planning for emerging technologies. The method makes use of contextual bandit problems to explore different routes to invest in while optimizing the operating cost or demand served. Two experiments are conducted. They (1) prove that the algorithm is better than random choice; and (2) show good performance with a gap of 3.7% relative to a heuristic solution to an oracle policy.

Optimal Integrated Model for Feeder Transit Route Design and Frequency-Setting Problem with Stop Selection

This study proposed a mathematical model for designing a feeder transit service for improving the service quality and accessibility of transportation hubs (such as airport and rail station). The proposed model featured an integrated framework, which simultaneously guided passengers to reach their nearest stops to get on and off the bus, designed routes to transport passengers from these selected pick-up stops to the transportation hubs, and calculated their departure frequencies. In particular, the maximum walking distance, the upper and lower limits of route frequencies, and the load factor rate of each route were fully accounted for in this study. The main objective of the proposed model was to simultaneously minimize the total walking, riding time, and waiting time of all passengers. As this study explored an NP-hard problem, a two-stage genetic algorithm combining the Dijkstra search method was further developed to yield metaoptimal solutions to the model within an acceptable time. Finally, a test instance in Chongqing City, China, demonstrated that the proposed model was an effective tool to generate a pedestrian, route, and operation plan; it reduced the total travel time, compared with the traditional model.

The use of mobile phone data in transport planning

The paper offers a historical review of the emergent use of individual location data from mobile phones in the field of transport planning that occurred over the past two decades. It identifies four key areas of development: 1) providing traffic information, 2) replacing the traditional data for transport planning, 3) enhancing our understanding of travel behaviour, and 4) validating transport models. It assesses the appropriate roles for mobile phone data in transport planning and suggests that the extraction of traffic information, the construction of origin-destination data, and transit route design have particularly practical implications for cities in the developing countries.

The use of mobile phone data in transport planning

The paper offers a historical review of the emergent use of individual location data from mobile phones in the field of transport planning that occurred over the past two decades. It identifies four key areas of development: 1) providing traffic information, 2) replacing the traditional data for transport planning, 3) enhancing our understanding of travel behaviour, and 4) validating transport models. It assesses the appropriate roles for mobile phone data in transport planning and suggests that the extraction of traffic information, the construction of origin-destination data, and transit route design have particularly practical implications for cities in the developing countries.

Solving urban transit route design problem using selection hyper-heuristics

The urban transit routing problem (UTRP) focuses on finding efficient travelling routes for vehicles in a public transportation system. It is one of the most significant problems faced by transit planners and city authorities throughout the world. This problem belongs to the class of difficult combinatorial problems, whose optimal solution is hard to find with the complexity that arises from the large search space, and the number of constraints imposed in constructing the solution. Hyper-heuristics have emerged as general-purpose search techniques that explore the space of low level heuristics to improve a given solution under an iterative framework. In this work, we evaluate the performance of a set of selection hyper-heuristics on the route design problem of bus networks, with the goal of minimising the passengers’ travel time, and the operator’s costs. Each selection hyper-heuristic is empirically tested on a set of benchmark instances and statistically compared to the other selection hyper-heuristics to determine the best approach. A sequence-based selection method combined with the great deluge acceptance method achieved the best performance, succeeding in finding improved results in much faster run times over the current best known solutions.

Prediction of bus passenger trip flow based on artificial neural network

The bus passenger trip flow is the base data for transit route design and optimization, and the characteristic of urban land use is the important factor for transit trip. However, the standard land use data are difficult to reflect the intensity of transit trip. This research proposed a method based on each zone building, land use situation, and bus accessibility to forecast the bus passenger trip flow in future period. Traffic zone is divided into three categories in accordance with the purpose of the residents travel: residential, commercial, and industrial. Then, by artificial neural network model, the three categories of the traffic zone bus passenger trip flow are forecasted. The method is assessed with the data of Dalian developing zone in China and results show its feasibility and reliability. Finally, the future research direction is discussed.

Multi-Objective Transit Route Network Design as Set Covering Problem

Many past researchers have ignored the multi-objective nature of the transit route network design problem (TrNDP), recognizing user or operator cost as their sole objective. The main purpose of this study is to identify the inherent conflict among TrNDP objectives in the design process. The conventional scheme for transit route design is addressed. A route constructive genetic algorithm is proposed to produce a vast pool of candidate routes that reflect the objectives of design, and then, a set covering problem (SCP) is formulated for the selection stage. A heuristic algorithm based on a randomized priority search is implemented for the SCP to produce a set of nondominated solutions that achieve different tradeoffs among the identified objectives. The solution methodology has been tested using Mandl's benchmark network problem. The test results showed that the methodology developed in this research not only outperforms solutions previously identified in the literature in terms of strategic and tactical terms of design, but it is also able to produce Pareto (or near Pareto) optimal solutions. A real-scale network of Rivera was also tested to prove the proposed methodology's reliability for larger-scale transit networks. Although many efficient meta-heuristics have been presented so far for the TrNDP, the presented one may take the lead because it does not require any weight coefficient calibration to address the multi-objective nature of the problem.

Flexible feeder transit route design to enhance service accessibility in urban area

SummaryTo improve the accessibility of transit system in urban areas, this paper presents a flexible feeder transit routing model that can serve irregular‐shaped networks. By integrating the cost efficiency of fixed‐route transit system and the flexibility of demand responsive transit system, the proposed model is capable of letting operating feeder busses temporarily deviate from their current route so as to serve the reported demand locations. With an objective of minimizing total bus travel time, a new operational mode is then proposed to allow busses to serve passengers on both street sides. In addition, when multiple feeder busses are operating in the target service area, the proposed model can provide an optimal plan to locate the nearest one to response to the demands. A three‐stage solution algorithm is also developed to yield meta‐optimal solutions to the problem in a reasonable amount of time by transforming the problem into a traveling salesman problem. Numerical studies have demonstrated the effectiveness of the proposed model as well as the heuristic solution approach. Copyright © 2015 John Wiley & Sons, Ltd.

Hybrid Algorithm for Route Design on Bus Rapid Transit Systems

In recent years, well-designed bus rapid transit (BRT) systems have become a real alternative to more expensive rail-based public transportation systems around the world. However, once the BRT system is operational, its success often depends on the routes offered to passengers. Thus, the bus rapid transit route design problem (BRTRDP) is the problem of finding a set of routes and frequencies that minimizes the operational and passenger costs (travel time) while simultaneously satisfying the system’s technical constraints, such as meeting the demands for trips, bus frequencies, and lane capacities. To address this problem, we propose a mathematical formulation of the BRTRDP as a mixed-integer program (MIP) with an underlying network structure. However, because of the vast number of routes, solving the MIP via branch and bound is out of reach for most practical instances. Hence, we propose a decomposition strategy that, given a certain set of routes, decouples the route selection decisions from the BRT system performance evaluation. The latter evaluation is done by solving a linear optimization problem using a column generation scheme. We embedded this decomposition strategy in a hybrid genetic algorithm (HGA) and tested it in 14 instances ranging from 5 to 40 stations with different BRT system topologies. The results show that in 8 of 14 problems, the HGA was able to obtain a solution that is provably optimal within 0.20%. Additionally, in 4 of 14 instances, HGA obtained the optimal solution.

Transit Route Design in the Emerging City Unit around High-Speed Rail Station

Most high-speed rail stations are generally built in the periphery of the city. Therefore, it is necessary for the government to build new transit routes to meet the traffic demand in the emerging city unit around the high-speed rail stations. This paper formulates a model based on genetic algorithms (GA) to design transit route considering various evaluation standards. Numerical example is set up to illustrate the model and algorithm.

Transit route and frequency design: Bi-level modeling and hybrid artificial bee colony algorithm approach

This paper proposes a bi-level transit network design problem where the transit routes and frequency settings are determined simultaneously. The upper-level problem is formulated as a mixed integer non-linear program with the objective of minimizing the number of passenger transfers, and the lower-level problem is the transit assignment problem with capacity constraints. A hybrid artificial bee colony (ABC) algorithm is developed to solve the bi-level problem. This algorithm relies on the ABC algorithm to design route structures and a proposed descent direction search method to determine an optimal frequency setting for a given route structure. The descent direction search method is developed by analyzing the optimality conditions of the lower-level problem and using the relationship between the lower- and upper-level objective functions. The step size for updating the frequency setting is determined by solving a linear integer program. To efficiently repair route structures, a node insertion and deletion strategy is proposed based on the average passenger demand for the direct services concerned. To increase the computation speed, a lower bound of the objective value for each route design solution is derived and used in the fitness evaluation of the proposed algorithm. Various experiments are set up to demonstrate the performance of our proposed algorithm and the properties of the problem.

Hybrid large neighborhood search for the bus rapid transit route design problem

Due to an increasing demand for public transportation and intra-urban mobility, an efficient organization of public transportation has gained significant importance in the last decades. In this paper we present a model formulation for the bus rapid transit route design problem, given a fixed number of routes to be offered. The problem can be tackled using a decomposition strategy, where route design and the determination of frequencies and passenger flows will be dealt with separately. We propose a hybrid metaheuristic based on a combination of Large Neighborhood Search (LNS) and Linear Programming (LP). The algorithm as such is iterative. Decision upon the design of routes will be handled using LNS. The resulting passenger flows and frequencies will be determined by solving a LP. The solution obtained may then be used to guide the exploration of new route designs in the following iterations within LNS. Several problem specific operators are suggested and have been tested. The proposed algorithm compares extremely favorable and is able to obtain high quality solutions within short computational times.

Simple and Effective Solution Methodology for Transit Network Design Problem

Transit Network Design Problem (TNDP) is the most important component in Transit planning and operation, in which the overall cost of the public transportation system highly depends on it. The main purpose of this study is to develop a simple and effective solution methodology for the TNDP, which goes beyond previous traditional sophisticated approaches. The solution methodology adopted in this research for the TNDP is based on partitioning the solution into two consecutive stages; Transit route Network Design Problem “TrNDP” stage and frequency setting stage. In the first stage; a deterministic solution for TrNDP is tackled to construct bus routes. The deterministic manner of the TrNDP solution relies on using linear and integer mathematical formulations that can be solved exactly with their standard solvers. In the second stage; bus frequencies are optimized among bus routes (obtained in stage 1) via Genetic Algorithm, for a total bus fleet size representing operator's main cost. The adopted solution methodology has been tested through Mandl’s benchmark transit network problem. The test results showed that the methodology developed in this research is able to provide and effective solution in terms of the number of constructed routes, the direct demand coverage, and the total travel time. General Terms Transportation engineering, optimization, transit route design, frequency setting, integer programming, genetic algorithm.

Transit Route Design Solved with Wireless Data Collection Algorithms

The transit route network design problem and its variants, aiming for economically viable and efficient transit routes in urban areas, has been attracting many contributions in recent years and continues to be an active research field. This activity is mainly caused by the intractability of the underlying optimization problems in large-scale transportation networks, generally leading to the use of heuristic schemes to build good practical solutions. A relationship between the transit route network design problem and the problem of data collection in wireless sensor networks is presented, and an innovative solution approach is proposed based on a recently proposed algorithm in that domain. The original solution algorithm is adapted to urban transportation networks and tests are carried out on large-scale data sets on a single-route variant of the transit route network design problem. The results obtained show that the solution approach can be efficiently tuned to meet specific route design objectives and provide balanced solutions.

Safety models incorporating graph theory based transit indicators

There is a considerable need for tools to enable the evaluation of the safety of transit networks at the planning stage. One interesting approach for the planning of public transportation systems is the study of networks. Network techniques involve the analysis of systems by viewing them as a graph composed of a set of vertices (nodes) and edges (links). Once the transport system is visualized as a graph, various network properties can be evaluated based on the relationships between the network elements. Several indicators can be calculated including connectivity, coverage, directness and complexity, among others. The main objective of this study is to investigate the relationship between network-based transit indicators and safety. The study develops macro-level collision prediction models that explicitly incorporate transit physical and operational elements and transit network indicators as explanatory variables. Several macro-level (zonal) collision prediction models were developed using a generalized linear regression technique, assuming a negative binomial error structure. The models were grouped into four main themes: transit infrastructure, transit network topology, transit route design, and transit performance and operations. The safety models showed that collisions were significantly associated with transit network properties such as: connectivity, coverage, overlapping degree and the Local Index of Transit Availability. As well, the models showed a significant relationship between collisions and some transit physical and operational attributes such as the number of routes, frequency of routes, bus density, length of bus and 3+ priority lanes.

On an exact method for the constrained shortest path problem

The constrained shortest path (CSP) is a well known NP-Hard problem. Besides from its straightforward application as a network problem, the CSP is also used as a building block under column-generation solution methods for crew scheduling and crew rostering problems. We propose an exact solution method for the CSP capable of handling large-scale networks in a reasonable amount of time. We compared our approach with three different state-of-the-art algorithms for the CSP and found optimal solutions on networks with up to 40,000 nodes and 800,000 arcs. We extended the algorithm to effectively solve the auxiliary problems of a multi-activity shift scheduling problem and a bus rapid transit route design problem tackled with column generation. We obtained significant speedups against alternative column generation schemes that solve the auxiliary problem with state-of-the-art commercial (linear) optimizers. We also present a first parallel version of our algorithm that shows promising results.