Bus Timetables Research Articles

Bus Timetabling Optimisation Method with Passenger Waiting Time and Dwell Time for Different Bus Passenger Capacity

The primary aim of this research is to solve the timetabling optimisation problem of bus service with multiple bus types consideration. A bus timetabling optimisation model with bi-objective functions is constructed. The first objective is to minimize the total bus dwell time for all bus stops along the route. The second objective is to minimize the total passenger waiting time for all passengers at all bus stops. Both objectives are considered simultaneously in the model by using weighted optimisation model with one objective function. Genetic algorithm is used to decide the bus departure time and type of bus used for every trip. Moreover, the passenger demand distribution is used to simulate the bus dwell time at all bus stops in the algorithm. Real-life bus passenger data such as passenger demand is used to demonstrate the efficiency of the proposed timetable and the productiveness of the timetabling optimisation model. The proposed optimal bus timetable from optimisation model is able to reduce the total dwell time by 14.88% and the total passenger waiting time by 23.55% compared with the bus timetable currently used. The result shows that the constructed bus timetabling optimisation model is effective to provide a reliable bus timetable by balancing the benefits between the bus passengers and operators.

Optimization of Correspondence Times in Bus Network Zones, Modeling and Resolution by the Multi-agent Approach

Urban transportation, especially bus transportation, is an important sign of development in every city in the world. The average waiting time for passengers at correspondence stations of buses is one of the most important measures of effectiveness of bus transportation. To the best of our knowledge, the studies in the literature are about maximizing the number of synchronizations in those correspondence stations whose objective is to minimize the waiting time in the network. The classical definition of synchronization used in the literature related to a time window. In this work, we introduce a new definition of synchronization of two buses in network zones. Within this context, we present a mathematical formulation of the synchronization bus timetabling problem as a multi-objective program, where we use the new meaning for synchronization of two buses in the network zones. Since the problem is NP-hard, we adapt a multi-agent approach to solve it. Numerical experiments show that after adapting the multi-agent approach using our proposed definition, we obtain high-quality solutions compared to the classical definition.

Integrated optimization for feeder bus timetabling and procurement scheme with consideration of environmental impact

Undesirable environmental impact of public transport (PT) and saving operational cost are of major importance to deal with. For a design of a new bus line, this plays a significant role on which fleet to purchase. The objectives of this work are to integrate vehicle procurement scheme and timetabling for a new feeder bus line with the constraint of purchasing budget, so as to (i) not only meet passenger demand, but also minimize their waiting time, and (ii) minimize the operator’s costs, including the consideration of environmental impact of PT, which can be measured by the penalty function of greenhouse gas emissions. A bi-objective optimization model for a feeder bus line is formulated to minimize operating cost and to minimize passengers’ waiting time. Timetables with even and uneven headway as well as single and multiple vehicle types are compared to verify the effectiveness of the proposed model. The solution approach is based on a decomposition heuristic method to tackle the procurement scheme and vehicle assignment, and on a real-number-coding genetic algorithm to generate departure times for each trip. Buses in three different types are considered: a mini electric, a liquefied natural gas, and a hybrid diesel with capacities of 35, 50, and 70 passengers, respectively. A case study in Beijing shows that, compared with the current timetable, the optimized MT-Uneven-headway timetables save 70.4% and 54.2% passengers’ waiting time and reduces the total cost by 64.1% and 31.2% for the morning and evening peak hours, respectively, indicating that the integrated method is effective.

Single bus line timetable optimization with big data: A case study in Beijing

Bus lines are suffering from serious decline in passenger volume due to the rapid development of urban rail transit and shared transport, and big data intelligence may help them change the status quo. However, the tremendous amount of travel data collected in recent years have not got effectively utilization. In order to improve passenger volume for bus lines, this paper devotes to develop a data-driven bus timetable to substitute the existing experience-based bus timetable, which is now widely used by bus lines. Driven by the bus GPS data and IC card data, a timetable optimization model with time-dependent passenger demand and travel time among stops is proposed. The objective of maximizing passenger volume is based on a new preference-based passenger selection model. The working hours constraint is initially formulated, and the headway constraint and departure time constraints are also taken into account. For handling the step functions in both objective and constraints, we introduce a set of 0–1 variables to transform the proposed model into an integer linear programming. A model contraction approach is provided for solving the medium-scale problems and a two-stage solution method is proposed for the large-scale problems. The proposed model and methodology are tested on a real-world bus line in Beijing. The results show that it is able to produce a satisfactory timetable that outperforms the previously used experience-based one in terms of raising the average passenger volume by 8.2%.

Integrated Approach to Vehicle Scheduling and Bus Timetabling for an Electric Bus Line

AbstractTimetable and vehicle scheduling are important for transit operations. Electric buses are more environmentally friendly compared with conventional buses, and have been developing rapidly an...

Models and a solution algorithm for planning transfer synchronization of bus timetables

Mixed-integer linear programming models and a heuristic algorithm are proposed for the transfer synchronization planning of bus timetables. The most important novelty of the proposed methodology is that the bus timetables and passenger choices of travel paths are simultaneously optimized in the model. The possibilities of transfer synchronization are fully explored in the timetable optimization because passengers select their paths to the destination in response to the timetables. Hence, bus timetables can be planned with high precision, and bus timetabling can be improved.

Sensitivity Analysis Model Bus Timetabling Problem by Considering Passenger Ease

The optimal Model Bus Timetabling Problem (MBTP) is a simplification of bus scheduling cases where a bus departure schedule with the optimal number of trips will be searched. The optimal number of trips is defined by maximizing the profit function of the bus by considering constraints such as the availability of buses, the executing officers, the number of passengers and the travel time. This model was developed by adding the constraints of ease of passengers in remembering the schedule. The model is then modified and completed using the branch and bound algorithm. The result of the sensitivity analysis to the model shows that the addition of bus or employee by the manager will not be directly proportional to the addition of passengers. So the addition of buses and employees is done when passengers increase. In addition, the addition of constraints ease passengers in reading the time table will make the increase in corporate profits with the consequences of decreased levels of passenger comfort. If the company chooses to improve the comfort of passengers it will affect the increase of the company's operational costs.

Feeder Bus Timetable Design and Vehicle Size Setting in Peak Hour Demand Conditions

This study proposes a solution to the feeder bus timetabling problem, in which the terminal departure times and vehicle sizes are simultaneously determined based on the given transfer passengers and their arrival times at a bus terminal. The problem is formulated as a mixed integer non-linear programming (MINLP) model with the objective of minimizing the transfer waiting time of served passengers, the transfer failure cost of non-served passengers, and the operating costs of bus companies. In addition to train passengers who plan to transfer to buses, local passengers who intend to board buses are considered and treated as passengers from virtual trains in the proposed model. Passenger attitudes and behaviors toward the waiting queue caused by bus capacity constraints in peak hour demand conditions are explicitly embedded in the MINLP model. A hybrid artificial bee colony (ABC) algorithm is developed to solve the MINLP model. Various experiments are set up to account for the performance of the proposed model and solution algorithm.

Bus timetabling considering passenger satisfaction: An empirical study in Beijing

Public transport is the key component in sustainable transportation. In China, large subsidies are provided by the national and local governments to encourage the use of public transport systems. However, high commuting demand leads to such incredible phenomenon that passengers have to climb windows for riding in buses during peak hours. Thus, a case study was conducted in a Beijing’s transport hub which covers 116 bus lines and ten stations. The existing bus timetabling is surprisingly manually made and 26.53% of the bus supply undertakes 59.19% of the bus demand, which results in unbearable long waiting time and on-board crowding. To find a bus timetabling method which is practical and applicable in China, three procedures are carried out. Firstly, the passenger satisfaction and the bus transit efficiency are simplified and formulated. Secondly, a preliminary bus timetabling method with consideration of passenger satisfaction is proposed for optimizing the bus frequency and headway. Thirdly, the bus timetabling is optimized by embodying a balance between the passenger satisfaction and the bus transit efficiency, subject to the constraints of load factor. This method is verified by field data and the results demonstrate that it has the advantage of reducing waiting time and lessening on-board discomfort.

Mathematical Analysis and an Exact Solution Combined with Preprocessing Method for Resynchronizing of Bus Timetable Problem

Bus timetabling is a subproblem of bus network planning, and it determines departure time of each trip of lines to make vehicles from different lines synchronously arrive at transfer stations. Due to the well-designed coordination of bus timetables, passengers can make a smooth transfer without waiting a long time for connecting buses. This paper addresses the planning level of resynchronizing of bus timetable problem allowing modifications to initial timetable. Timetable modifications consist of shifts in the departure times and headways. A single-objective mixed-integer programming model is proposed for this problem to maximize the number of total transferring passengers benefiting from smooth transfers. We analyze the mathematical properties of this model, and then a preprocessing method is designed to reduce the solution space of the proposed model. The numerical results show that the reduced model is effectively solved by branch and bound algorithm, and the preprocessing method has the potential to be applied for large-scale bus networks.

Multiconstrained Timetable Optimization and Performance Evaluation in the Presence of Travel Time Noise

AbstractThis work addresses the problem of bus timetabling under multiple operational constraints with the objective of minimizing the waiting time variations of passengers at stops. It places spec...

A minimum-cost model for bus timetabling problem

In urban traffic, a bus’ running speed is greatly influenced by the time-dependent road conditions. Based on historical GPS data, this paper formulates a bus’ running speed between each pair of adjacent stops as a step function. A minimum-cost timetabling model is proposed, in which the total operation cost consists of the cost for a fixed setup and that for variable fuel consumption. Furthermore, a genetic algorithm with self-crossover operation is used to optimize the proposed integer nonlinear programming model. Finally, a real-world case study of Yuntong 128 bus line in Beijing is presented. Comparisons among popular timetabling models are given, involving time-dependent running speed, minimum running speed, maximum running speed and average running speed. The results demonstrate that the consideration of time-dependent running speed is helpful to improve the prediction accuracy of the fuel consumption cost by around 12.7%.

Performance comparison of genetic algorithms and particle swarm optimization for model integer programming bus timetabling problem

Genetic Algorithm (GA) is a common algorithm used to solve optimization problems with artificial intelligence approach. Similarly, the Particle Swarm Optimization (PSO) algorithm. Both algorithms have different advantages and disadvantages when applied to the case of optimization of the Model Integer Programming for Bus Timetabling Problem (MIPBTP), where in the case of MIPBTP will be found the optimal number of trips confronted with various constraints. The comparison results show that the PSO algorithm is superior in terms of complexity, accuracy, iteration and program simplicity in finding the optimal solution.

Developing an urban public passenger transport route network with account for natural resource limitations

Abstract The article proposes a procedure for accounting natural resource (areal) limitations when developing an urban public passenger transport (UPPT) route network, consisting of two blocks. The first block is dedicated to the assessment of the number of route vehicle units simultaneously located in parking areas arranged at terminal stops of UPPT routes, based on areal limitations of the urban territory intended for transport (transport lands). To solve the tasks of this block, probabilistic statistical methods and mathematical analysis methods were used. Data on bus timetables in Perm which allowed making conclusions of the possibility to introduce additional schedules or experimental routes in the city served as an information base. In the second block, the area of transport lands is reduced by avoiding duplication of UPPT routes. This block is based on mathematical statistics methods. Data on the existing UPPT route network in Perm served as an information base.

Bus Timetable Design to Ensure Smooth Transfer and Avoid Delay Propagation

Bus Timetable Design to Ensure Smooth Transfer and Avoid Delay Propagation

AUGMENTED REALITY-ASSISTED NAVIGATION IN A UNIVERSITY CAMPUS

Augmented reality, AR in short, refers to the integration of digital information, like text, multimedia content, GPS data, etc, with the real-world user environment in real time. In this way, as the term implies, the existing, perceived environment is augmented by computer-generated sensory input overlaid in the form of new, complementary information on top of it. Augmented reality differs from virtual reality in the sense that it does not induce a new artificial environment but it enhances existing reality with additional elements of information – real or virtual - of relative semantic content. Augmented reality was initially used in the context of military, medical and industrial applications, but soon its use expanded into several additional areas including entertainment and games, education, culture, architecture and construction, commerce, advertising. Sophisticated augmented reality applications utilize advanced technologies like computer vision and object/gesture recognition so that users can interact or even digitally alter their real environment. However, simpler augmented reality applications for smartphones exploit GPS and compass functionalities for determining user location and orientation and by means of an augmented reality “marker” they project digital information on the display of the user device. In this work, we present MyARCampus, an augmented reality application for mobile handheld devices, like mobile phones or tablets. MyARCampus uses image recognition techniques for context awareness and aims to facilitate student life within a university campus. In particular, our application provides detailed information about course timetables for classrooms as well as detailed information about bus timetables for bus-stops located within the Patras University Campus and can be easily extended to cover additional points of interest. Such information is usually provided in the form of printed, static announcements. However, updates and changes are rarely included timely in these printouts. Using MyARCampus, students can obtain anytime useful information about classroom and bus timetables which can highly facilitate course attendance and transportation within the campus and provide invaluable support for efficient schedule organization and management . Furthermore, such an application can be extremely useful, especially in the cases of users who do not speak the local language or do not even understand the signs that are distributed in the deployment location.

Spatiotemporal model for assessing the stability of urban human convergence and divergence patterns

ABSTRACTUnderstanding the stability of urban flows is critical for urban transportation, urban planning and public health. However, few studies have measured the stability of aggregate human convergence or divergence patterns. We propose a spatiotemporal model for assessing the stability of human convergence and divergence patterns. A mobile phone location data set obtained from Shenzhen, China, was used to assess the stability of daily human convergence and divergence patterns at three different spatial scales, i.e. points (cell phone towers), lines (bus lines) and areas (traffic analysis zones [TAZs]). Our analysis results demonstrated that the proposed model can identify points and bus lines with time-dependent variations in stability, which is useful for delineating TAZs for transportation planning, or adjusting bus timetables and routes to meet the needs of bus riders. Comparisons of the results obtained from the proposed model and the widely used entropy measure indicated that the proposed model is suitable for assessing the differences in stability for various types of spatial analysis units, e.g. cell phone towers. Therefore, the proposed model is a useful alternative approach of measuring spatiotemporal stability of aggregate human convergence and divergence patterns, which can be derived from the space–time trajectories of moving objects.

Mobility Management for Health Promotion in Cooperation with Local Government Urban Transport Planning and Public Health Departments

Background Due to recent progress in motorization, transport has become much more convenient. However, the reductions in physical activity caused by car- dependent lifestyles have led to deteriorations in people's health. Mobility management programs have been introduced to Japan and some European countries to address social problems caused by car overuse. Mobility management is a transport measure targeted at households and individuals, which is being applied in companies and schools. It is aimed at promoting voluntary behavior modification, encouraging people to switch from cars to sustainable modes of transport. Although mobility management is a helpful way to improve health by increasing physical activity, it has only been used as an urban transport measure since its introduction to Japan. Urban transport and public health departments have not collaborated to improve public health through mobility management. Methods In this study, we implemented a mobility management program as part of an existing local government health-counseling program. The health instruction program is based on the national health insurance system (2008). This article discusses the introduction of a mobility management program in Yamato-city, Kanagawa, Japan. Well-designed tools for the mobility management program are: a motivational leaflet, behavioral planning sheet, bus-route map, bus and rail timetable, weight and pedometer record, pedometer, town guide for active travel, and a reflector for night walks. Previously, counseling guidance was mainly composed of two lectures. One lecture was about healthy eating habits, the participants ate a 500 kcal lunch cooked by a dietician for 300 yen. The other lecture consisted of a simple stretching and strength-building workout, which can be conducted at home. We encouraged participants to increase their physical activity levels by distributing our mobility management tools during the both lectures. Results We collected before and after questionnaires from 41 people, and compared them to determine the impact of the mobility management experiment. Bus and bicycle use increased significantly as a whole purpose. As purposes of a social or recreational activity, car use decreased significantly and walking and bus use increased significantly. Conclusions The results show that mobility management is an effective urban transport and public health measure, as physical activity increased and car use decreased. The program developed for this study can easily be used in other cities. Next summer, mobility management, we will evaluate health indicators by collecting medical examination data before and after this program.

Bi-level programming model for multi-modal regional bus timetable and vehicle dispatch with stochastic travel time

Public transit can be interrupted by emergencies, which can prevent vehicles from completing a trip on time, making travel times stochastic variables. In this paper, an uncertain bi-level programming model for multi-modal regional bus timetables and vehicle dispatch is presented by assuming travel times follow normal distribution based on GPS data for automatic bus station system. The lower model studies coordinating the timetables in regional bus transit with multiple modes of transport, by considering transfers between buses and buses, subways, special passenger lines with intersecting routes. The upper model assigns trips from several routes to buses located at different depots to minimize operating costs; some restrictions such as parking capacity are considered in this model. A group of feasible solutions generated by the lower level plan is provided for input into the upper level plan in order to compare their best solutions. The lower and upper solutions are calculated using the Estimation of Distribution Algorithm, which defines the satisfactory degree of the solutions to reduce their search space to find optimal scheme quickly.

Integer programming model for optimizing bus timetable using genetic algorithm

Bus timetable gave an information for passengers to ensure the availability of bus services. Timetable optimal condition happened when bus trips frequency could adapt and suit with passenger demand. In the peak time, the number of bus trips would be larger than the off-peak time. If the number of bus trips were more frequent than the optimal condition, it would make a high operating cost for bus operator. Conversely, if the number of trip was less than optimal condition, it would make a bad quality service for passengers. In this paper, the bus timetabling problem would be solved by integer programming model with modified genetic algorithm. Modification was placed in the chromosomes design, initial population recovery technique, chromosomes reconstruction and chromosomes extermination on specific generation. The result of this model gave the optimal solution with accuracy 99.1%.