Bus Trips Research Articles

A model for the periodic optimization of bus dispatching times

We model the problem of dispatching time control in rolling horizons following a periodic optimization approach reactionary to travel time and passenger demand disturbances. This model provides more flexibility to transport planners allowing them to adjust the bus schedules during the daily operations. We prove that our periodic optimization model is a convex quadratic program, guaranteeing the global optimality of its solution. To reduce the computational burden, we introduce an iterative algorithm that uses gradient approximations to obtain an approximate dispatching solution. The proposed solution method is found to be significantly faster than exact optimization approaches for quadratic programming and maintains an (almost) negligible optimality gap in realistic bus operation scenarios. Finally, we show that our periodic optimization method outperforms myopic methods that adjust the dispatching time of each bus trip in isolation using operational data from bus line 302 in Singapore.

Why do (or don’t) people carpool for long distance trips? A discrete choice experiment in France

Abstract Long-distance carpooling is an emerging mode in France and Europe, but little is known about monetary values of this mode attributes in transport economics. We conducted a discrete choice experiment to identify and measure the values of attributes of long-distance transport modes for a trip as a driver and as a passenger, with a special focus on carpooling. Around 1.700 French individuals have been surveyed. We use discrete mixed logit models to estimate the probability of mode choice. We find that the value of travel time for a driver who carpools is on average 13% higher than the value of travel time when driving alone in his/her car. The average value of travel time for a carpool trip as passenger is around 26 euros per hour, 60% higher than for a train trip and 20% higher than for a bus trip. Moreover, our study confirms a strong preference for driving solo over taking carpoolers in one’s car. We also show that individuals traveling as carpool passenger incur a “discomfort” cost of on average 4.5 euros per extra passenger in the same vehicle. Finally, we identify robust socio-economic effects affecting the probability of carpooling, especially gender effects. When they drive a car, females are less likely to carpool than male, but they prefer to carpool two passengers over only one passenger.

Bus driver scheduling enhancement: a derandomizing approach for uncertain bus trip times

The bus driver scheduling problems aim to optimally deploy drivers to fulfil published timetables for bus services subject to drivers’ contractual working rules. In this study, we first develop an integer linear programming model for a practical driver scheduling problem. We proceed to formulate another integer linear programming model for the driver scheduling enhancement to incorporate with uncertain bus travel times, which are addressed by an effective derandomizing approach. Moreover, the lower and upper bounds of the developed models are investigated by using the Lagrangian problems with which the solution quality can be evaluated. To assess the efficiency and applicability of the developed models, we conduct a case study based on historical operational data of the bus route #95 in Singapore. Finally, we perform a necessary sensitivity analysis of the bus fleet size and driver workload on the bus driver scheduling problems to identify some valuable managerial insights.

Analysis of Bus Trip Characteristic Analysis and Demand Forecasting Based on GA-NARX Neural Network Model

Passenger flow is the basis for bus operation scheduling. Huge advances are being made to develop smart city traffic using big data. Intelligent bus systems based on bus integrated circuit (IC) card systems are constantly developing and improving. Compared with traditional manual survey data, bus IC data is low-cost, real-time and accurate with a simple acquisition method. Bus IC data is an important basic data resource and data mining of bus IC cards can obtain dynamic information about urban bus passenger flow and help improve urban bus planning and service levels. The crucial factor in determining whether this data can be reasonably applied to the optimization of urban bus systems is whether spatial and temporal characteristics of the passenger bus trip can be obtained through bus IC data mining, and there is much current research interest into this topic. In this paper, the characteristics of one-day passenger flow and time-division passenger flow are analyzed based on data obtained from swiping IC cards for one week on a bus in Qingdao. Then, based on a GA-NARX neural network model, the passenger flow is forecast using the IC card swipe data for five working days of Qingdao No. 1 bus (using ten minutes as the time interval). The forecasting results show that the passenger flow can be successfully predicted using this method and thus this method can be used for short-term passenger flow forecasting using bus IC cards.

Calculating the speed of city bus trips: The case of Ljubljana, Slovenia

Calculating the speed of city bus trips: The case of Ljubljana, Slovenia

Bus Scheduling of Overlapping Routes With Multi-Vehicle Types Based on Passenger OD Data

To mitigate the problems caused by bus route overlap in the transit network, this paper proposes a new scheduling method with both large and small vehicle types based on passenger OD (Origin-Destination) data. The minimum of total cost of passenger travel time and bus company operation is taken as the optimization objective, departure intervals and vehicle types are taken as the optimization variables. The impact of route overlap on passenger travel time is analyzed. A heuristic algorithm is developed to solve the optimization model to produce the departure time and vehicle type for each bus trip. Finally, three real bus routes in Harbin city are taken as an example to validate the proposed model using peak-hour data. Compared with the model without considering route overlap, the proposed model can reduce total passenger travel time and cost by 5.2% and 8.8% respectively.

Baseball Connections

Baseball Connections John Shorey (bio) I gaze out the window from the front passenger seat of the motor coach, watching the recently tasseled cornfields whiz by. It is midmorning on a late July day in America's heartland. Seated across the aisle is my friend and colleague, Bill Ricketts. Behind me, dispersed throughout the bus, are twenty-two baseball fans, ranging from retired couples to recently graduated high school seniors and everything in between. We are on day three of a week-long baseball class/tour through the Midwest. As the bus rambles along I-70 between Kansas City and St. Louis, an excerpt from the "First Inning" of Ken Burn's documentary Baseball plays on the video monitors, and I think to myself, It doesn't get much better than this. Just about every baseball fan has experienced the generational connections of baseball within their family. I know I have. But on this occasion, I shared my love of baseball with twenty-two people I had not met until two days before. By the end of the week, the connections inspired by baseball forged a special bond within the group. For me, baseball is about connections. It connects me with my childhood; it connects me with my parents and grandfather, my brother and sisters, childhood friends, Little League, and trips to County Stadium in Milwaukee to watch my favorite player, Henry Aaron. It connects me with my recent past and my present; this includes my wife and daughters, friends, colleagues, fantasy baseball, attending games ranging from t-ball, to collegiate, the minor leagues, and the majors. Baseball connects me with our country's history and culture. The game has definitely impacted the course of US history. The first large group gathering in New York City after 9/11 was the Mets versus Braves baseball game. A Mike Piazza home run late in the game gave New Yorkers something to cheer about after the long days of suffering and grief. Baseball also helped launch the civil rights movement. Shortly before his death in 1968, the Reverend Martin Luther King Jr. remarked, "Jackie Robinson made my success possible. Without him, I never would have been able to do what I did." Baseball is interwoven throughout our culture, from baseball caps to dozens of baseball phrases [End Page 16] used in everyday conversions. Baseball is ubiquitous throughout our culture, which lays the foundation for how baseball promotes connections. Over a recent twenty-year span of my life, I was fortunate enough to experience connections through baseball in a very unique way. For two decades, I developed and taught a course called "Baseball and American Culture" at Iowa Western Community College. The idea for the class was inspired by an article I came across in a professional journal about a college course offered by the University of Southern Maine, in which the professor and class went on a week-long bus trip traversing the Northeast to tour various baseball sites and attend games. With that as my inspiration, I thought, "Why not develop a Midwest version of the class?" I needed a cohort to help teach and handle the logistics of a week-long trip. I turned to friend and fellow instructor Bill, and he agreed to help develop the course with me. The wheels for the class started in motion early in the spring semester of 1998. The timing to start a baseball class couldn't have been better. At that time the United States was embroiled in the Clinton impeachment proceedings; the country needed a lift and a distraction. As in many other times throughout our history, baseball came to the rescue. Excitement was building for the upcoming Major League Baseball season in anticipation of a possible run at Roger Maris's single-season home run record. Bill and I had to really sell our community college president and the executive board on our idea of getting paid to teach a class on baseball. We jokingly told our president that if we caught a Mark McGwire home run ball, we would donate it to the college if the class and week-long trip was approved. The class was approved. Little did...

Added value of a customized transit app for metropolitan bus trips

Bus passengers require reliable and real-time information but the existing systems still do not fully satisfy their needs. Within the framework of the European project HARMONY, a transit app has been developed in Madrid Region to improve the information that passengers receive from metropolitan buses. It enables an option for passengers to send incidences to operators in real-time. A SWOT matrix has been built and a two-stage consultation to passengers has been carried out to know the current transit app market and to detect gaps in the users’ needs, leading to the features to be implemented. None of the existing apps allow a bi-directional communication between operators and passengers. Survey results reveal that apps like Google Maps do not compete with specific transit apps that include real-time information because daily commuters require that specific information of their routes.

Travel Behavior of the Urban Poor: A Comparative Study between Maputo and Nairobi

Commuters from poor households face increased challenges in their daily mobility in Sub-Saharan African cities, yet studies on their travel behavior are rare. Most poor individuals resort to walking long distances because they cannot afford motorcycles, bicycles, or even a single bus trip. Using person-trip data, this paper tries to investigate travel behavior of the urban poor through a comparative study between Maputo City and Nairobi City. The logistic regression results showed some similarities and variations among the factors affecting the travel behavior of the urban poor in Maputo City and Nairobi City. The likelihood of choosing Non-Motorized Transport (NMT) over public transport decreased with ownership of motorcycles and private cars in both cities. Surprisingly, gender had no impact on the likelihood of selecting NMT either in Maputo or Nairobi. This might indicate that men and women are equally likely to walk. Variations were observed with respect to the effect of age and residence location. For example, while the likelihood of selecting NMT is positively correlated with the residential location in Maputo, a negative relationship was observed for Nairobi. More explanations for these similarities and variations on the travel behavior are discussed in the article

Forecasting bus ridership using a \u201cBlended Approach\u201d

As sources of “Big Data” continue to grow, transportation planners and researchers seek to utilize these new resources. Given the current dependency on traditional transportation data sources and conventional tools (e.g., spreadsheets and propriety models), how can these new resources be used? This research examines a “blended data” approach, using a web-based, open source platform to assist transit agencies to forecast bus ridership. The platform is capable of incorporating new Big Data sources and traditional data sources, using modern processing techniques and tools, particularly Application Programming Interfaces (APIs). This research demonstrates the use of APIs in a transit demand methodology that yields a robust model for bus ridership. The approach uses the Census Transportation Planning Products data, modified with American Community Survey data, to generate origin–destination tables for bus trips in a designated market area. Microsimulation models us a transit scheduling specification (General Transit Feed Specification) and an open source routing engine (OpenTripPlanner). Local farebox data validates the microsimulation models. Analyses of model output and farebox data for the Atlantic City transit market area, and a scenario analysis of service reduction in the Princeton/Trenton transit market area, illustrate the use a “blended approach” for bus ridership forecasting.

An exact method for the bus dispatching problem in rolling horizons

In this work, we adapt the rolling-horizon approach of Eberlein et al. (2001) for adjusting the dispatching times of buses at each rolling horizon. The industry practice is to adjust the dispatching time of a bus once it departs from the first stop while considering that future trips will operate as planned. In contrast, a rolling-horizon approach adjusts simultaneously the dispatching times of all trips that operate during a pre-determined time interval resulting in a coordinated effort to maintain the target headways. Due to the increased number of dispatching time decisions, this coordinated effort increases the computational burden. To reduce the computational cost, we introduce a nonlinear program and we propose a novel reformulation that limits the recursive relations of the optimization problem. Our program is proved to be convex and can be solved to global optimality under a limited computational cost. In addition, it outperforms myopic methods that adjust the dispatching time of each bus trip in isolation. The sensitivity of our method to travel time and passenger demand fluctuations is investigated on a simulation scenario of bus line 15L in Denver.

Incorporating weather conditions and travel history in estimating the alighting bus stops from smart card data

Origin-destination flow of passengers in bus networks is a crucial input to the public transport planning and operational decisions. Smart card systems in many cities, however, record only the bus boarding information (namely an open system), which makes it challenging to use smart card data for origin-destination estimations and subsequent analyses. This study addresses this research gap by proposing a machine learning approach and applying the gradient boosting decision tree (GBDT) algorithm to estimate the alighting stops of bus trips from open smart card data. It advances the state-of-the-art by including, for the first time, weather variables and travel history of individuals in the GBDT algorithm alongside the network characteristics. The method is applied to six-month smart card data from the City of Changsha, China, with more than 17 million trip-records from 700 thousand card users. The model prediction results show that, compared to classic machine learning methods, GBDT not only yields higher prediction accuracy but more importantly is also able to rank the influencing factors on bus ridership. The results demonstrate that incorporation of weather variables and travel history further improves the prediction capability of the models. The proposed GBDT-based framework is flexible and scalable: it can be readily trained with smart card data from other cities to be used for predicting bus origin-destination flow. The results can contribute to improved transport sustainability of a city by enabling smart bus planning and operational decisions.

Travel-Time Prediction of Bus Journey With Multiple Bus Trips

Accurate travel-time prediction of public transport is essential for reliable journey planning in urban transportation systems. However, existing studies on bus travel-/arrival-time prediction often focus only on improving the prediction accuracy of a single bus trip. This is inadequate in modern public transportation systems, where a bus journey usually consists of multiple bus trips. In this paper, we investigate the problem of travel-time prediction for bus journeys that takes into account a passenger’s riding time on multiple bus trips, and also his/her waiting time at transfer points (interchange stations or bus stops). A novel framework is proposed to separately predict the riding and waiting time of a given journey from multiple datasets (i.e., historical bus trajectories, bus route, and road network), and combining the results to form the final travel-time prediction. We empirically determine the impact factors of bus riding times and develop a long short-term memory model that can accurately predict the riding time of each segment of the bus lines/routes. We also demonstrate that the waiting time at transfer points significantly impacts the total journey travel time, and estimating the waiting time is non-trivial as we cannot assume a fixed distribution waiting time. In order to accurately predict the waiting time, we introduce a novel interval-based historical average method that can efficiently address the correlation and sensitivity issues in waiting time prediction. Experiments on real-world data show that the proposed method notably outperforms six baseline approaches for all the scenarios considered.

Research on Urban Interconnection Index of Transportation Card Industry

In order to intuitively reflect the operation situation and forecast the future trend of the traffic card industry, and enhance the data support ability for the service evaluation of the traffic card industry. Referring to the relevant research results, this paper combines the operating needs of the card companies and the construction principle of the index system, and constructs the service index system of the card industry based on the big data of the card industry, which can be used to monitor the operation situation of the card industry and the service status of the bus trip. It can also be applied to the industry supervision, business monitoring and users. Travel service. Then, taking the traffic card interconnection index as an example, this paper evaluates the status and causes of the provincial traffic card interconnection by using the data analysis platform of Guangdong Province’s traffic card, and puts forward the prospects for the construction of the comprehensive service index of the traffic card industry.

Bus rescheduling in rolling horizons for regularity-based services

Given the unstable nature of the bus operations, the regularity of the bus services cannot be maintained throughout the day. Especially in densely populated areas, the inherent variability of the trip travel times affects the regularity of the daily services since the arrival times of several trips at stops might oscillate significantly from their expected values. Because of the high level of uncertainty, this work proposes a periodic rescheduling of the dispatching times of the daily bus trips to adjust continuously to the operational changes. A key differentiator from previous works is that a periodic rescheduling does not focus only on the running buses, but also reschedules the dispatching times of all remaining daily trips while considering operational constraints related to layover times and capacity limits. Typical scheduling problems (such as a bus scheduling problem) are NP-hard because of the discrete nature of the decision variables. Hence, the computational burden prohibits their solution with analytical methods. Catering for capacity limits increases further the complexity of our problem because it results in a non-smooth objective function which is not differentiable at every point of its domain. For this reason, this work proposes a sequential hill climbing method to search the solution space more efficiently and reschedule the dispatching times of trips in near real time. This approach is tested using real-time data from bus line 15 L in Denver, USA. Critical issues, such as to what extent can the periodic dispatching time rescheduling improve the regularity of daily operations, are investigated.

Robust Network-Wide Bus Scheduling With Transfer Synchronizations

Travel time and demand disturbances lead to unreliable bus operations and missed passenger transfers. This study formulates the multi-line synchronization problem as a robust min(i)max problem that considers the fluctuations of the travel and dwell times of bus trips. Given the infeasibility of the multi-line synchronization problem in extreme cases of travel/dwell time disturbances, we introduce a flexible problem formulation that incorporates the constraint violations into the objective function. To produce a robust schedule, the dispatching times of trips are our design variables and the travel and dwell time fluctuations are the environmental variables which have an adversarial role in our minimax problem. We validate our approach in the bus network of The Hague using 1 month of actual vehicle location and passenger counting data. There, we demonstrate the potential improvement in terms of service regularity and increased synchronizations in common case and extreme case conditions.

Regional Electric Bus Driving Plan Optimization Algorithm considering Charging Time Window

Currently, although pure electric buses have the advantage of environmental-friendly, its endurance mileage is insufficient and the charging pile is still far away from the actual demand, resulting in a more complicated scheduling. Given this, we studied the driving plan of a pure electric bus, aiming to support the promotion and application of the electric bus. Considering service quality, we built a regional pure electric bus driving plan model and designed an optimal solution based on packing idea and genetic algorithm, aiming at minimizing fleet size, charging facility, and empty driving mileage. We took the electric bus routes operated in a region of Beijing as an empirical example. Compared with the results from the greedy algorithm, we found that the total cost of 544 bus trips with tasks was reduced by 19.6%. Although the average empty driving mileage increased by approximately 20%, the number of pure electric bus vehicles and the required amount of charging infrastructure decreased by 19.7% and 33.3%, respectively. The cost of increasing empty driving mileage was lower than that of the reducing number of buses and charging facilities, indicating that the above three variables reached a balance, and the optimization algorithm is proved to be significantly effective.

Impact of the dedicated infrastructure on bus service quality: an empirical analysis

ABSTRACTAn econometric analysis is conducted to quantify the impact of different variables on the service quality of the Santiago, Chile bus system. The indicators tested as measures of service quality are the average bus speed, the bus trip time coefficient of variation and the bus headway coefficient of variation. The analysis uses peak hour data obtained for all the routes served by the system’s various concessionaire operators. A separate multiple linear regression model is estimated for each indicator, with the latter as the explained variable. The main explanatory variables are a series of design factors representing different types of dedicated route infrastructure and the incorporation in some routes of segments of urban motorway. The results of the models show that the existence of dedicated bus route infrastructure positively impacts all three service quality indicators. The use of motorway segments in particular has a major positive effect on average speed. The model estimates also reveal that the main explanatory factor in headway variability at the end of a route is the headway variability at the start of it, the latter factor determined by operator management decisions regarding bus despatches.

Introducing autonomous buses and taxis: Quantifying the potential benefits in Japanese transportation systems

The introduction of autonomous buses and taxis is expected to generate such benefits as cost reductions—and particularly for regional bus operations with a substantial deficit—as well as enhancing public transit accessibility through decreased trip costs. The purpose of this paper is to provide an overview of the impacts of introducing autonomous buses and taxis on metropolitan transportation systems by quantifying the costs of travel in Japan, and to discuss the potential benefits. First, this study sets the assumptions on autonomous driving technology, including its impacts on vehicle costs, the decreased labor costs for driving and safety monitoring in buses and taxis, and decreased driving stress for private car users. Next, operating costs are computed for autonomous buses and taxis in Japanese metropolitan areas. The costs of travel, or the sum of monetary and time costs, are then computed with and without vehicle automation for different trip types in high- and low-density metropolitan areas. The results highlight that the costs of public transit trips that currently have a smaller share of time costs in overall trip costs could decrease considerably due to vehicle automation. For instance, costs for 10–20-km trip lengths could decrease by 44–61% for taxi trips and 13–37% for rail/bus trips with taxi access, followed by a decrease of 6–11% for bus trips and 1–11% for rail trips with bus access. Further, private car trip costs could decrease by 11–16%. More substantial cost reductions in rail/bus trips with taxi access could occur in the case of smaller trip distances and/or in residential areas far from stations; larger reductions in rail trips with bus access could occur in low-density metropolitan areas. Finally, it is expected that vehicle automation in more fixed modes of public road transit could primarily benefit the transit industry and government, with such effects as improved labor productivity and reduced subsidies, while vehicle automation in more flexible modes could benefit metropolitan residents as well as the transit industry. This further suggests that a deficit of regional bus operations could be recovered during the transition to the full performance of autonomous buses.

PENERAPAN METODE IPA (IMPORTANCE PERFOMANCE ANALYSIS) UNTUK MENGANALISIS KEPENTINGAN DAN KEPUASAN PENUMPANG TERHADAP KUALITAS PELAYANAN BUS TRANS KOETARADJA (STUDI KASUS : KORIDOR I KEUDAH – DARUSSALAM)

The use of private vehicles that increases continuously due to the poor of public transport services is one of the causes of congestion. To solve this problem, the City Government of Banda Aceh provides Trans Koetaradja Bus. The limitations of existing public transport services in the city of Banda Aceh caused the community to attempt facilitate their own movements with private vehicles. So that the development of Trans Kutaraja is faced with the challenge of how Trans Kutaraja is able to attract private vehicle users who are the majority of road user in the city of Banda Aceh. therefore, it is important to know how the user respondses to the service quality of Trans Kutaraja. This study aims to analyze the quality of Trans Koetaradja Bus services and determine the quality of Trans Koetaradja Bus services of attributes that are prioritized based on the perception of the users of Trans Koetaradja Bus by using the Importance Performance Analysis Method consisting of quadrant analysis and gap analysis. From the results of the quadrant analysis the IPA method for Trans Koetaradja users is more focused on the factors that most influence on the quality of bus services. It can be seen from the indicators located in quadrant I (top priority) consisting of indicators such as the availability of trash can at the bus stop, ease of obtaining information related to the route and time of bus arrival, the accuracy of bus travel schedules, time speed spent on bus trips from one point to another and the time period of arrival between one fleet and another.