Investigating impacts of environmental factors on the cycling behavior of bicycle-sharing users

As it is widely accepted, cycling tends to produce health benefits and reduce air pollution. Policymakers encourage people to use bikes by improving cycling facilities as well as developing bicycle-sharing systems (BSS). It is increasingly interesting to investigate how environmental factors influence the cycling behavior of users of bicycle-sharing systems, as users of bicycle-sharing systems tend to be different from regular cyclists. Although earlier studies have examined effects of safety and convenience on the cycling behavior of regular riders, they rarely explored effects of safety and convenience on the cycling behavior of BSS riders. Therefore, in this study, we aimed to investigate how road safety, convenience, and public safety affect the cycling behavior of BSS riders by controlling for other environmental factors. Specifically, in this study, we investigated the impacts of environmental characteristics, including population density, employment density, land use mix, accessibility to point-of-interests (schools, shops, parks and gyms), road infrastructure, public transit accessibility, road safety, convenience, and public safety on the usage of BSS. Additionally, for a more accurate measure of public transit accessibility, road safety, convenience, and public safety, we used spatiotemporally varying measurements instead of spatially varying measurements, which have been widely used in earlier studies. We conducted an empirical investigation in Chicago with cycling data from a BSS called Divvy. In this study, we particularly attempted to answer the following questions: (1) how traffic accidents and congestion influence the usage of BSS; (2) how violent crime influences the usage of BSS; and (3) how public transit accessibility influences the usage of BSS. Moreover, we tried to offer implications for policies aiming to increase the usage of BSS or for the site selection of new docking stations. Empirical results demonstrate that density of bicycle lanes, public transit accessibility, and public safety influence the usage of BSS, which provides answers for our research questions. Empirical results also suggest policy implications that improving bicycle facilities and reducing the rate of violent crime rates tend to increase the usage of BSS. Moreover, some environmental factors could be considered in selecting a site for a new docking station

An Empirical Performance Comparison of Meta-heuristic Algorithms for School Bus Routing Problem

School Bus Routing Problem is an NP-hard Combinatorial Optimization problem. Thus, mega-heuristic algorithms are widely used to solve instances of the School Bus Routing Problem with large data. In this work we present a model of the School Bus Routing Problem and empirical performances comparison between three meta-heuristic algorithms named Simulated Annealing (SA), Tabu Search (TS) and Ant-Colony Optimization (ACO) on the problem. We have analyzed their performances in terms of solution quality. The results show that all three algorithms have the ability to solve the School Bus Routing Problem. In addition, computational results show that TS performed best when execution time is not restricted while ACO had relative good performance when time is restricted but poor when the time is unrestricted.Keywords:  School Bus Routing Problem; Combinatorial Optimization; Meta-heuristic Algorithm

School Bus Routing To Allow Later School Start Times

School districts providing busing services for students who live too far to walk to school. In many districts a fleet of school buses is used in sequence to transport high school students, then middle school students, and then elementary school students. The result is that high school classes must start much earlier in the morning than the elementary school, and buses may traverse similar routes three times each morning and afternoon. In light of recent research on the benefits of later high school start times and the need to control transportation costs, school districts are seeking efficient school bus routing plans that meet student needs at low cost. This study uses 2018 data for schools in Northampton, Massachusetts, to identify the potential to achieve two objectives: 1) start the high school classes as late as possible in the day, and 2) minimize the cost of busing. The proposed procedure makes use of existing school bus data to optimize bus routes, which can be applicable for smaller cities. A revised routing plan that mixes high school and middle school students on the same buses allows the high school to start 30 minutes later while reducing total school bus operations

Mathematical Formulation Model for a School Bus Routing Problem with Small Instance Data

This paper aims to describe the mathematical formulation model and an exact optimal solution analyses for a school bus routing problem with small instance data. The formulated model has been used  to compute the optimal solution of time spent by students at all bus stops, apart from that the bus stops are not necessary be linearly ordered. We also listed down five procedures of mathematical formulation model to reach an exact optimal solution for a school bus routing problem with small instance data. We assume that each bus has fixed pick up points, these generates the many possible routes for a bus, the number of routes that generated is equal to permutation of pick up points, for each route of a bus we computing the objective function and the route with smallest objective function value can be optimal route of a bus. The sample data from two schools located at Dar es Salaam are collected and validated in the model to shows the good performing of that model. The optimal solution results obtained shows that the students spent minimal minutes in new planned routes compared to current routes. Keywords: bus stop, students, buses, optimal value, optimal solution, set, pick up

Modelling School Bus in Favor of Needy Student: The Conceptual Framework

Surabaya city is not yet have a school bus system. With a numerous needy student, the government is realized that free school bus can support the needy student in their transportation cost. The purpose of this research is make a model that can be used by the government to designs the most optimal route and optimal distribution of the free school bus. This paper is focusing on the conceptual framework of the processes in finding the optimal route and analyzing the chosen route. The optimal route is a route that covers the area with the most number of potential passengers, the needy student. The needy density layer is the primary data that will accompanied with street layer, school layer, and bus depot layer. Not just finding the route, the chosen route then used in an accessibility analyst in order to find its effect of the school bus in existing transportation system. The result of this research indicates that the model in this project can be used to find the best route and can support the government in making a decision in the limitation they have

Solving school bus routing and student assignment problems with heuristic and column generation approach.

In this dissertation, we solve a school bus routing problem of transporting students including special education (handicapped) students and assigning them in Oldham county education district. The main contribution of this research is that we consider special education students (Type-2) along with other students (Type-1) and design a comprehensive school bus schedule to transport both kinds of students at the same time. Also, a student assignment mathematical model is presented to optimize the number of bus stops in use as well as one important measure of service quality, the total student walking distance. Comparing to the classic clustering methods, heuristic methods, or other methods from previous literatures, a mathematical optimization model is developed to solve a student assignment problem and to obtain the global optimal solution. The modeling constraints include budget limit, travel time limit, equity, school time window, and etc. Especially, the main difference between our model and other models is that it takes Type-2 students into consideration along with critical constraints accordingly, and solves the resulting more complex problem. Moreover, the school bus routing model in this work is one of the most general optimization models representing the school bus routing problem. On the other hand, similar to all existing models, the developed model considers the total system cost as the objective function value to minimize, different bus capacities, and common vehicle routing constraints such as flow conservation on routes and subtour elimination. Furthermore, another main difference is that the bus scheduling and school time window is also considered and solved in the model. With two different types of students, both Type-1 and Type-2, the time restrictions are varying, resulting in more complexity and additional constraints. The results in this work present the difficulties of meeting the requirement of Type-2 student riding time limit and school time window simultaneously. Also, the constraints regarding service equity and quality are provided and they can be used by decision makers if necessary. Either densely populated urban areas or sparsely populated rural areas, the school bus routing problem is difficult to solve due to a large number of students or long travel distance. The school bus routing problem falls under vehicle routing problem (VRP) with additional requirements because each student represents one unit of capacity. In this dissertation, we present a modeling framework that solves a student assignment problem with bus stop selection, and subsequently a school bus routing problem with school time window constraints. We demonstrate the efficacy of heuristic methods as well as a column generation technique implemented to solve the problems using real data

Bus Transportation Analysis for a Changed Start Time in the Millbury Public Schools

The Millbury Public School System is examining the feasibility of later school start times for their adolescent students. Busing transportation is frequently cited as the major logistical set back in this change. This study analyzes four different potential transportation strategies to help guide informed decision making when determining busing during this district wide adjustment. The four options identified were: swapping start times among schools, pushing all start times later, reducing the number of iterations a bus takes, and purchasing additional buses to allow the high school to start at the same time as another school