Bi-Objective Optimization of Integrated Design of Dynamic Wireless Charging Facility and Onboard Battery Size for Electric Bus Systems
Tingting Zhao, University of South FloridaShow Abstract
Zhiqiang Wu, University of South Florida
Yu Zhang, University of South Florida
In this work, we propose a bi-objective optimization problem formulation for integrated dynamic wireless charging facility (DWCF) location and on-board battery size design of electric bus systems. Objectives are to minimize DWCF deployment cost and reduce energy consumption of electrified systems by selecting the optimal battery size for electric buses. As larger on-board battery size leads to higher energy consumption during bus operation but requires lower density of DWCF deployment and vice versa, the two objectives contradict each other. As energy consumption is also a major part of daily operations of an electric bus system, this formulation is also a trade-off between short-term facility deployment cost and long-term operational cost. A weighted sum method was applied to solve the proposed bi-objective integer programming problem. The results of the case study of four routes of the Greater Attleboro Taunton Regional Transit Authority (GATRA) in Massachusetts verified that the proposed problem formulation and weighted sum method could solve this problem efficiently and provide decision-makers with various options regarding DWCF design and battery size choice. An additional criterion for comparison between the solutions on the Pareto frontier taking the surplus of battery energy level compared to the lower bound of energy level limit was taken into consideration. One solution result of the bi-objective optimization method (selected according to the battery energy level surplus criteria) was compared with the solution of a single objective model in the literature from both energy-consumption and life-cycle cost perspectives to provide more managerial insights for decision-makers and researchers.
Dynamic Bus Scheduling Based on Real-Time Demand and Travel Time
Anil Kumar, Former research associate, IIT MadrasShow Abstract
Hari Prasath, Former Undergraduate Student Department of Civil Engg. IIT Madras
Lelitha Vanajakshi, Indian Institute of Technology, Madras
Provision of a reliable and convenient bus service is a vital component of any successful public transportation system. One of the important steps in this direction is to develop a bus schedule that can meet prevailing demand. A simple solution is to prepare a fixed bus schedule with a constant headway. However, as passenger demand varies over time and space, a fixed schedule with a constant headway may lead to inadequate number of buses during peak periods and under-utilization of the system in off-peak periods, which may not be beneficial for the operator. In addition to this, variability in traffic conditions may lead to irregularities in adhering to the predefined schedule, making the users wait longer or miss bus. To overcome these problems, the present study proposes a Demand and Travel Time Responsive (DTR) model to maximize the benefit of the operator by preparing an optimal schedule that can adapt to the variations in passenger demands and traffic conditions in real-time, subjected to minimizing the waiting time of the passengers, capacity constraints of the buses to achieve the maximum financial benefit as well as social satisfaction. For this, the study analyses the data received from real-time tracking devices that were fitted in a selected bus route in Chennai to keep track of the vehicles, identify their locations along routes to estimate the future states, compare them with the planned service, and to evaluate alternative measures to improve the performance of the system.
Characterizing the Importance of Criminal Factors Affecting Bus Ridership Using Random Forest Ensemble Algorithm
Qing Li, Texas Department of TransportationShow Abstract
Fengxiang Qiao, Texas Southern University
Andrew Mao, Texas Department of Transportation
Catherine McCreight, Texas Department of Transportation
Public transit systems provide mass movement with substantial traffic operational and environmental benefits. Despite these benefits, it still represents a small market share in the United States. A comprehensive understanding of the determinants of transit ridership is essential for investment allocation to improve safety, mobility, and air quality in an urban area. Except socio-economic factors, crime has been identified as a determinant for the ridership. Most studies found that ridership and crime are linearly correlated to each other, whereas other studies believed the level of crime can result in a nonlinear effect on the ridership. The relationship between ridership and crime remains inconclusive. Besides, the simultaneous relationship between ridership and crime is scarcely addressed and most ridership studies only include one or a few external factors that affect crime opportunity. This paper proposes a Random Forest based feature selection method to characterize the importance of multiple variables, to bus ridership and total crime, respectively, at different levels. A case study in Houston, Texas, USA, for the year 2017 is provided to illustrate the feature selection and modeling process. A total of 110,885 crimes, ridership on 9,004 bus stops, and related socio-economic information were collected. Results indicated, at a medium or lower level of ridership, it is positively correlated to crime; the linear relationship can be broken down at a high level; reducing the total crime per capita can promote bus ridership. Random Forest based models were developed with the selected determinants, performing with high accuracy in ridership per capita estimates.
A Practical Approach for Evaluating and Scheduling a Campus Bus System Based on Clustering
Ahmadreza Mahmoudzadeh, Texas A&M UniversityShow Abstract
Sayna Firoozi Yeganeh, University of Tehran
Barrett Ochoa, Texas A&M University
Xiubin Bruce Wang, Texas A&M University
Justin Tippy, Texas A&M University
Optimal fixed-route transit scheduling can help transit planners and managers save finite resources and increase service efficiency. Compared with urban transportation systems, a university campus transit system can carry specific characteristics such as high service frequencies, overcrowding and capacity concerns, and unique challenges in service allocation, which have not yet been widely studied. The Texas A&M transit service is investigated in this text in order to optimize its large network of 18 on and off-campus routes. The main aim of the paper is to find a practical scheduling methodology for the campus system. A comprehensive analysis of ridership data is performed to evaluate the delay, running time, on-time performance, and efficiency of the current network. Then, two clustering methods of hierarchical and K-means are used to categorize the ridership data considering the class times. The data is analyzed to determine the similar patterns in both weekday and weekend services. Then homogeneous clusters within each day are detected to find the trip departure times. A concept of the generous, high-frequency system is defined to assign the bus trips to the new departure times. Results show that the current university bus system could operate more efficiently during class changes; currently, only even headways are provided regardless of periods with higher demand. The proposed methodology could potentially increase the efficiency of the system by up to 40 percent.
A Method to Classify Bus Bunching Events Using AVL Data
Wenzhe Sun, Kyoto UniversityShow Abstract
Jan-Dirk Schmoecker, Kyoto University
On routes without external control, bus bunching tends to persist for several stops, often even until the terminal. For the operator, however, identifying the initial bunching point is of primary importance. In previous work we proposed an approach to predict bunching which performs well in detecting whether two buses are bunched or not, but we show in this paper that the approach does not perform well to identify initial bunching only. This study therefore tries to distinguish initial bunching and following ones by learning the bunching patterns based on AVL (Automatic Vehicle Location) data, and more importantly, to identify the predictability of initial bunching. Initial bunching is divided into gradual bunching and sudden bunching according to the fluctuation patterns in the dwell time and travel time. We show that gradual initial bunching can be predicted several stops ahead. Some leading indicators useful for initial bunching detecting are obtained by a set of analytical formulations, and verified by analyzing AVL data from Kyoto, Japan.
Impact of Individual Passenger Degree of Circuity on Optimal Transit Network Design
Young-Jae Lee, Morgan State UniversityShow Abstract
Amirreza Nickkar, Morgan State University
Mana Meskar, Sharif University of Technology
Transit network design or transit routing is the most fundamental work in transit planning and operation because it results in demand attraction, revenue, operating costs and overall transit network efficiency. So, it is fundamental work for transit agencies and has been a significant research topic. In this research, the trade-off relationship between individual travel time costs affected by Degree of Circuity (DOC) of transit routings and total transit costs consisting of total passenger travel costs and total vehicle operating costs is examined using the optimal transit routing algorithm developed in the authors’ previous research. The results showed that with higher maximum individual DOC constraint, total costs dropped due to the relaxation of constraints of the optimization process, which allows finding a better solution. In addition, due to the higher DOC, total passenger travel costs went up, but fewer vehicles were required and total operating costs were lower due to more circuitous vehicle routings. However, for the example used in the research, total costs were unchanged after DOC of 3.0, at which the individual maximum DOC constraint becomes meaningless for the example. Although total costs declined until DOC of 3.0, the decline pattern diminished drastically from DOC of 1.5. So, it is a planning and administrative decision as to which DOC of 1.5, 2.0, 2.5 or 3.0 should be chosen to save total costs knowing some passengers will make sacrifices in the form of longer travel time due to higher DOC.
Optimization of Electric Bus Regional Operation Plan
Muyang Lu, Beijing Jiaotong UniversityShow Abstract
Enjian Yao, Beijing Jiaotong University
Due to the increasing pollution of fuel vehicles to the environment and the rising cost of their usage, using electric buses to replace fuel vehicles has become a new idea for the development of public transportation. However, due to the limitation of the cruising range of electric buses and the impact of charging demand on driving, the formulation of driving plans is more complicated than that of fuel vehicles. Considering the optimal driving plan and reasonable layout of charging facilities for electric bus, this paper optimizes the total operating cost of electric bus and its supporting facilities. Based on genetic algorithm, a set of operational planning methods is proposed. Finally, the model effect was verified by taking some Bus Line operations in Daxing District of Beijing as example, and compared with the traditional bus operation plan, the total operating cost was reduced by 16.77%.
Analysis and Application of Log-Linear and Quantile Regression Models to Predict Bus Dwell Times
Travis Glick, Portland State UniversityShow Abstract
Miguel Figliozzi, Portland State University
Understanding the key factors that contribute to transit travel times and travel time variability is an essential part of transit planning and research. Delay that occurs when buses service bus stops, dwell time, is one of the main sources of travel time variability and has therefore been the subject of ongoing research to identify and quantify its determinants. Previous research has focused on testing new variables using linear regressions that may be added to models to improve predictions. An important assumption of linear regression models used in past research efforts is homoscedasticity or the equal distribution of the residuals across all values of the predicted dwell times. The homoscedasticity assumption is usually violated in linear regressions models of dwell time and this can lead to inconsistent and inefficient estimations of the independent variable coefficients. Log-linear models can sometimes correct for the lack of homoscedasticity, i.e. for heteroscedasticity in the residual distribution. Quantile regressions, which predict the conditional quantiles, rather than the conditional mean, are non-parametric and therefore more robust estimators in the presence of heteroscedasticity. This research furthers the understanding of established dwell determinants using these novel approaches to estimate dwell and provides a relatively simple approach to improve existing models at bus stops with low average dwell times.
Evaluation and Prediction of Bus Operation Condition Based on Bunching Analysis
Yajuan Deng, Chang'an UniversityShow Abstract
Yanfeng Ma, Chang'an University
Xin Luo, Chang'an University
Rui Ma, University of California, Davis
Yuejiao Wang, Chang'an University
To improve the efficiency of bus operation and quality of service, it is essential to evaluate and predict the bus operation conditions. This study employed automatic vehicle location (AVL) data of Xi’an city, the time-headway taken as a key indicator, to evaluate the bus operation condition and make further predictions from the perspective of bus bunching. The K-means method is adopted to classify the bus operation condition to obtain the threshold of bus bunching, and the Markov chain is employed to describe the condition evolution, additionally a multiple logistic model is developed to predict condition in subsequent time, furthermore, the space distribution of the micro bus operation condition is employed to explore the macro bus operation condition. The results show that the micro operation condition can be classified as bus bunching, bus bunching transition, normal condition, large interval transition and large interval, whereas the macro operation condition can be categorized into cluster, pre-transition condition, post-transition condition and normal condition. The evolution of the micro condition display the property of probability, and bunching is a dynamic process of generation and dissipation. Further, a considerably high accuracy of condition prediction can be observed with the multiple logistics model. It is more conclusive to evaluate the macro operation condition by bus bunching space distribution.
Multi-Objective Optimal Formulations for Public Transit with Headway-Based Holding Control Considering Bus Fleet Size
Shidong Liang, University of Shanghai for Science and TechnologyShow Abstract
Minghui Ma, Jilin University
Shengxue He, University of Shanghai for Science and Technology
In recent years, with the development of advanced technologies, real-time bus control strategies have been implemented to improve the daily operation of transit systems. Especially headway-based holding control which is a proven strategy to reduce bus bunching and improve service reliability for high-frequency bus routes, with the concept of regulating headways between successive buses. However, the traditional headway-based control method only focused on the regulation of bus headways, without considering the number of buses on the route. The control method may fail to regulate the buses headways due to there being too few buses on the bus route, even though the control method itself is effective. Therefore, this work has presented a set of optimal control formulations to minimize the costs for the passengers and the bus company through calculating the optimal number of buses and the dynamic holding time, taking into account the randomness of passenger arrivals. A set of equations were formulated to obtain the operation of the buses with headway-based holding control or the schedule-based control method. The effects of this optimization method were tested under different operational settings. It was found that the model was capable of reducing the costs of the bus company and passengers through utilizing headway-based bus holding control combined with optimization of the bus fleet size. The proposed optimization model could minimize the number of buses on the route for a guaranteed service level, alleviating the problem of redundant bus fleet sizes caused by bus bunching in the traditional schedule-based control method.
A Joint Panel Binary Logit and Fractional Split Model for Converting Route-Level Data to Stop-Level Data
Moshiur Rahman, University of Central FloridaShow Abstract
Shamsunnahar Yasmin, University of Central Florida
Naveen Eluru, University of Central Florida
Detailed ridership analytics platform requires refined data on transit ridership to understand factors affecting ridership (at the stop and/or route-level). However, detailed data for stop-based boarding and alighting information are not readily available for the entire bus system. Transit agencies usually resort to compiling ridership data on a sample of buses operating on the various routes. We propose an approach to infer stop-level ridership for transit systems that only compile route-level ridership information. A joint model structure of binary logit and fractional split model is proposed to estimate stop-level ridership data from route-level ridership. The model is developed for the Greater Orlando region with ridership data for 8 four-month time periods from May 2014 through December 2016. In the presence of repeated data measures, panel version of the joint econometric models for boarding and ridership dimensions are estimated. The development of such an analytical framework will allow bus systems with only route-level ridership data to generate stop-level ridership data. The model results offer intuitive results and clearly supports our hypothesis that it is feasible to generate stop-level ridership with route-level ridership data. The proposed model can be employed by transit agencies without stop-level data to estimate stop-level ridership metrics.
An Integrated Approach to Vehicle Scheduling and Bus Timetabling for Electric Bus Line
Tong Chen, Tongji UniversityShow Abstract
Jing Teng, Tongji University
Shuangjun MA, Shanghai Bus Company
Timetable and vehicle schedule are important for transit operation. Electric buses are environmentally friendly compared with conventional buses, developing rapidly and may replace conventional buses in cities. This paper focuses on the timetabling and vehicle scheduling problem for electric buses. A multi-objective optimization model is proposed for a single electric bus line. The model comprehensively considers passenger demand, bus operators’ costs and social benefits. The objectives are to minimize the standard deviation of departure intervals each period, the number of vehicles and the charging costs. As for constraints, the model takes the range of departure intervals, vehicle operation mileage, electricity price of different period, charging time and charging conditions into consideration. This model can be solved by the multi-objective particle swarm optimization (MOPSO) algorithm. Then the paper proposes a strategy to select an actual optimal solution from the Pareto optimal solutions set. In the case, compared to existing schedule and segrated schedule, the integrated model can reduce the number of vehicles and charging costs, as well as relatively increase the smoothness of departure intervals. Moreover, the vehicle charging periods distribute during electricity off-peak hours, which can improve the utilization efficiency of the electricity.
Customer Satisfaction of Bus Transit with Bus Lanes: A Case Study of Shanghai, China
Chen Qian, Tongji UniversityShow Abstract
Linghui He, Key Laboratory of Road and Traffic Engineering of the Ministry of Education
Zhengyu Duan, Tongji University
Dongyuan Yang, Key Laboratory of Road and Traffic Engineering of the Ministry of Education
To support the reconstruction of bus lanes, a customer satisfaction survey of bus transit was conducted based on passenger experience. The survey aimed to understand how bus passengers perceive bus lanes and which factors influence their level of satisfaction. The survey was conducted in April 2017, obtaining 1,860 valid samples along 16 bus lanes. An ordinal regression model was built to explain passenger satisfaction with the service quality of bus lanes. Two types of factors, including bus system operation indicators and passenger attributes, are presented in the model with different influences. It is found that the demands of passengers who work in government, enterprise, or institution, or in liberal or self-employed professions, and who take the bus for commuting purposes need to be prioritized. It is necessary to pay more attention to bus ridership and the waiting time at bus stops along bus lanes when canceling old lanes and adding new lanes.
The Variation Features of Bus Ridership After the Opening of New Metro Lines: A Case Study in Xiamen, China
Dongyuan Yang, Key Laboratory of Road and Traffic Engineering of the Ministry of EducationShow Abstract
Zhe Li, Tongji University
Weifeng Li, Tongji University
Bus ridership may be affected by the opening of a new metro line. To support the decision-making related to the bus service adjustment, it is important to comprehensively understand the bus ridership variation features. Smart card data and GPS data collected in Xiamen, China are applied in this paper. The bus ridership variation is analyzed during the study period including one month before the opening of Xiamen Metro Line 1 and another after the opening. The iterated cumulative sums of squares algorithm is introduced to identify structural change points (SCPs) of consecutive bus ridership time series both at station level and line level. To understand the variation features, a discussion is carried out to analyze the temporal distribution, the spatial distribution and other associated factors of structural change points. Results show that the viariation of bus ridership is mainly detected along the metro corridor in the first week after the metro opening. The variation of the bus ridership is strongly associated with bus line adjustment and the spatial relationship to metro stations and moderately associated with land use around the metro stations. The methodology proposed and the conclusion drawn in this paper can serve as reference for the decision-making related to the bus operation in the context of metro opening.
Optimal Recharging Scheduling for a Fast-Charging Battery Electric Bus System Considering Electricity Demand Charges
Yi He, Utah State UniversityShow Abstract
Ziqi Song, Utah State University
Zhaocai Liu, Utah State University
Battery electric buses (BEBs) are rapidly being embraced by public transit agencies due to their environmental and economic benefits. To address the problems of limited driving range and time-consuming recharging for BEBs, manufacturers have developed rapid on-route charging technology that utilizes typical layovers at terminals to recharge buses in operation using high power. With on-route fast charging, BEBs are as capable as their diesel counterparts in terms of range and operating time. However, fast charging may result in high electricity demand charges, which are costs associated with peak electric power demand, thereby significantly increasing the fuel costs and reducing the economic attractiveness of BEBs. This study proposed a network modeling framework to identify an optimal recharging schedule for a fast-charging BEB system such that the demand charges are minimized. The recharging scheduling problem was first formulated as a nonlinear nonconvex program. The model was then reformulated as a linear program (LP), which is easy to solve even for real-world large-scale problems. Numerical studies based on two real-world bus networks were provided to demonstrate the effectiveness of the proposed model. With optimal recharging scheduling, demand charges for a fast-charging BEB system can be reduced by as much as 90%.
Understanding the Structure of Bus Travel Demand Using a Low-Rank and Sparse Matrix Decomposition Method
Dongyuan Yang, Key Laboratory of Road and Traffic Engineering of the Ministry of EducationShow Abstract
Zhe Li, Tongji University
Zhengyu Duan, Tongji University
Bus route planning, operation organization, and time dispatching can be informed by day-to-day variations in bus travel demand. In this study, the spatiotemporal origin-destination (OD) matrix is constructed using one month of smart card data from Xiamen Central City, China. A low-rank and sparse matrix decomposition method named the “GoDecomposition” (GoDec) algorithm is introduced to analyze the structure of bus travel demand. The raw OD matrix can be decomposed into three parts—a low-rank matrix, sparse matrix, and noise matrix—corresponding to three kinds of travel demand: periodic temporal flow, burst temporal flow, and random temporal flow. The low-rank matrix has periodicity; the sparse matrix appears at specific dates and locations; and the proportion of random temporal flow is slight. The reconstruction error of the low-rank matrix to the raw OD matrix is only 5.1%. The decomposition results are consistent with average daily OD matrix. However, the difference between them was large in a specific space (e.g., boarding zone/alighting zone/OD). The GoDec algorithm can be expanded to other kind of travel demand analysis, by using mobile phone data, floating car data, and so on.
Bus Travel Time Reliability Incorporating In-Stop Waiting Time and In-Vehicle Travel Time with AVL Data
Zixu Zhuang, Harbin Institute of TechnologyShow Abstract
Zhanhong Cheng, Harbin Institute of Technology
Jia Yao, Harbin Institute of Technology
Jian Wang, Harbin Institute of Technology
Shi An, Harbin Institute of Technology
Improving bus travel time reliability can attract more commuters to use bus transit, and therefore reduces the share of car and alleviates traffic congestion. This paper formulates a new bus travel time reliability metric that jointly considers the in-stop waiting time and in-vehicle travel time by the convolution of independent events’ probabilities. The new reliability metric is defined as the probability when bus travel time is less than a certain threshold; the threshold is determined by data to maximize the fluctuation of bus travel reliability within a day. Next, Automatic Vehicle Location (AVL) data of No.63 Bus Line in Harbin City is used to demonstrate the applicability of the proposed method. Results show that factors such as weather, workday, departure time, travel distance, and the distance from the boarding stop to the bus departure station will significantly affect the travel time reliability. The proposed bus travel time reliability metric is tested to be sensitive to the effect of different factors and can be applied in the evaluation and optimization of bus transit system.
Multi-Objective Approaches for Bus Route Design with Subsidy Consideration: An Empirical Study in Chaiyi City
Li-Wen Chen, MinJiang UniversityShow Abstract
Ta-Yin Hu, National Cheng Kung University
Le-Chi Shih, National Cheng Kung University
Smart City has been proposed to be a total solution for cities to improve economic and mobility activities. One of the important components of smart city is smart mobility and/or smart transport. Public transportation, as one of the basic element in a Smart City, provides shared transport service, such as bus, light rail transit (LRT), and mass rapid transit (MRT), to save energy, reduce air pollution and relieve congestion. For transit operators, how to provide efficient and effective service in traffic networks with limited budget is an important issue. As more bus routes are provided under the same budget, how to balance bus route and subsidy becomes a new issue. The research proposes a multi-objective formulation to design the optimal bus routes under three conflicting objectives, including travel cost, demand, and subsidy. Two solutions approaches, including the ε-constraint method and the bi-level programming method, are constructed to solve the problem. An empirical study is conducted based on a realistic network in Chiayi (Taiwan) to illustrate the proposed algorithms.
Modeling and Predicting the Cascading Effects of Delay in Transit Systems
Aparna Oruganti, Presbyterian HospitalShow Abstract
Sanchita Basak, Vanderbilt University
Fangzhou Sun, Facebook
Hiba Baroud, Vanderbilt University
Abhishek Dubey, Vanderbilt University
An effective real-time estimation of the travel time for vehicles, using AVL (Automatic Vehicle Locators) has added a new dimension to the smart city planning. In this paper, we used data collected over several months from a transit agency and show how this data can be potentially used to learn patterns of travel time during specially planned events like NFL (National Football League) games and music award ceremonies. The impact of NFL games along with consideration of other factors like weather, traffic condition, distance is discussed with their relative importance to the prediction of travel time. Statistical learning models are used to predict travel time and subsequently assess the cascading effects of delay. The model performance is determined based on its predictive accuracy according to the out-of-sample error. In addition, the models help identify the most significant variables that influence the delay in the transit system. In order to compare the actual and predicted travel time for days having special events, heat maps are generated showing the delay impacts in different time windows between two timepoint-segments in comparison to a non-game day. This work focuses on the prediction and visualization of the delay in the public transit system and the analysis of its cascading effects on the entire transportation network. According to the study results, we are able to explain more than 80% of the variance in the bus travel time at each segment and can make future travel predictions during planned events with an out-of-sample error of 2.0 minutes using information on the bus schedule, traffic, weather, and scheduled events. According to the variable importance analysis, traffic information is most significant in predicting the delay in the transit system.
Toward Optimized Deployment of Electric Bus Systems Using Cooperative ITS
Georgios Laskaris, University of LuxembourgShow Abstract
Marcin Seredynski, Volvo E-Bus Competence Center
Francesco Viti, University of Luxembourg
In this paper we analyze the impact of using cooperative intelligent transportation systems (C-ITS) to manage electrical bus systems. A simulation-based study is presented where three control strategies are used to regulate the operations of a line, namely bus holding, Green Light Optimal Dwell Time Adaptation (GLODTA) and Transit Signal Priority (TSP) and a hybrid controller combining holding and GLODTA. The results show, using a realistic scenario of a major line in Luxembourg City, that buses are efficiently operated without necessarily providing additional priority to public transport, hence without negatively affecting the capacity of the private vehicles system. The newely introduced controller reduces further the variation of headway compared to holding strategy, provides a more robust and shorter travel time and contributes in energy saving during operation.
The Effects of Arterial BRT-Lite Dwell Time on General Traffic and Intersection Capacity
Benjamin Tomhave, University of Minnesota, Twin CitiesShow Abstract
Yufeng Zhang, University of Minnesota, Twin Cities
Alireza Khani, University of Minnesota, Twin Cities
John Hourdos, University of Minnesota
Peter Dirks, University of Minnesota
This study aims to fill a perceived void in the research concerning the impact of Bus Rapid Transit (BRT) systems on surrounding traffic and intersection capacity. In particular, this paper focuses on the effect dwelling arterial BRT-“Lite” buses have on general traffic conditions and motor vehicle capacity in an effort to compare the impact of implementing mixed traffic BRT-“Lite” systems as opposed to exclusive lane “full service-BRT” or ordinary local buses that operate in homogenous traffic. Two traffic scenarios were studied, one for high volume oversaturated traffic occurring during the two-week period of the Minnesota State Fair, and a second for normal operating conditions where intersections were at or below saturation levels. Five primary traffic measures—green dwell time, queue length before and after bus arrival, and traffic flow rates before and after arrival—were extracted from video data collected at the busiest intersection along a BRT-Lite route. A three-dimensional point cloud was created to compare traffic conditions (queue length or flow rate) before and after bus arrival as a function of green dwell time and parsed into discrete clusters using k-means clustering. Using the aggregate behavior of this clustering analysis, in addition to multiple linear regression models, it was found that BRT-Lite buses have no significant impact—positive or negative—on intersection performance or surrounding traffic capacity.
Robust Stop-Skipping at the Tactical Planning Stage with Evolutionary Optimization
Konstantinos Gkiotsalitis, University of TwenteShow Abstract
The planning of stop-skipping strategies based on the expected travel times of bus trips has a positive effect in practice only if the traffic conditions during the daily operations do not deviate significantly from the expected ones. For this reason, we propose a non-deterministic approach which considers the uncertainty of trip travel times and provides stop-skipping strategies which are robust to travel time variations. In more detail, we show how historical travel time observations can be integrated into a Genetic Algorithm (GA) that tries to compute a robust stop-skipping strategy for all daily trips of a bus line. The proposed mathematical program of robust stop-skipping at the tactical planning stage is solved using the minimax principle, whereas the GA implementation ensures that improved solutions can be obtained even for high-dimensional problems by avoiding the exhaustive exploration of the solution space. The proposed approach is validated with the use of 5-month data from a circular bus line in Singapore demonstrating an improved performance of more than 10% in worst-case scenarios which encourages further investigation of the robust stop-skipping problem.
Optimal Design of Bus Routes for Different Vehicle Types Considering Various Driving Regimes and Environmental Factors
Yue SU, Southwest Jiaotong UniversityShow Abstract
Xiaobo Liu, Southwest Jiaotong University
Guo Lu, Southwest Jiaotong University
Wenbo Fan, Southwest Jiaotong University
As a major part of public transportation system, bus transit has been regarded as an effective mode to alleviate the traffic congestion and solve vehicle emission problem. The performance of bus transit system depends largely on its design of proper stop locations. In this reasearch, we proposed a multi-period continuum model (peak hour and off-peak hour) to optimize the design of a bus route for four different vehicle types (i.e., supercharge bus, Compressed Natural Gas (CNG) bus, Lithium-ion battery bus, and diesel bus) considering driving regimes and the pollutant cost. Inter-stop driving regimes, including acceleration, cruising, coasting, and deceleration, are explicitly introduced into the optimization to determine whether and how the coasting regime should be undertaken in the tradeoff between vehicle’s commercial speed and the operating cost. The comparison for the cost effectiveness of each alternative has been investigated in a life span with respect to different vehicle types. The method has been implemented in the real-word bus route 7 in Yaan City (China). The numerical experiments suggest that through optimization, the total system cost has been saved by more than 50%. The results of continuum model are validated by the comparison with the discretized results, and the outcomes are closely located in neighborhood (with error less than 3%). The life-cycle cost of four vehicle types is finally analyzed, and the result indicates that due to the high purchase prices, it’s difficult for clean-energy buses to outperform conventional buses in a life cycle (normally 8 years), unless with subsidies provided.
High-Quality Approximation Algorithms for Vehicle Synchronization in Transit Systems
Mojtaba Abdolmaleki, University of Michigan, Ann ArborShow Abstract
Neda Masoud, University of Michigan, Ann Arbor
Yafeng Yin, University of Michigan, Ann Arbor
This paper introduces efficient algorithms to synchronize vehicles in a transit network so as to minimize the total passenger transfer time. We first formulate the problem as an optimization problem with congruence constraints. We show that the problem is NP-hard, and investigate several special cases of the problem that are solvable in polynomial time. Furthermore, we show that the general problem is equivalent to the well-studied MAX CUT problem. As such, we use two well-known approximation algorithms for the max cut problem to solve the vehicle synchronization problem, and assess their quality on a real case study.
Controlling for Endogeneity Between Bus Headway and Bus Ridership: A Case Study of the Orlando Region
Moshiur Rahman, University of Central FloridaShow Abstract
Shamsunnahar Yasmin, University of Central Florida
Naveen Eluru, University of Central Florida
In this study, we develop an advanced econometric model that consider the potential endogeneity of stop level headway in modeling bus ridership. Lower headway for stops is generally determined by choice as these stops are expected to have higher ridership. We consider headway endogeneity by proposing a simultaneous equation system that considers headway and ridership in a joint framework. The proposed model is developed employing stop level ridership data from the Orlando region for 11 four-month time periods. The presence of multiple data points for each stop allows us to develop panel models for headway, boarding, and alighting. The headway variable is modeled using a panel ordered logit model while the ridership variables are modeled using a panel group ordered logit models. The model estimation results justify the consideration of headway endogeneity in bus ridership analysis. To illustrate the value of the proposed model, validation exercise and policy analysis are also conducted.
Operations of Zero-Emission Buses: Impacts of Charging Methods and Mechanisms on Costs and the Level of Service
Max2 Wiercx, Delft University of Technology
Raymond Huisman, Goudappel Coffeng
Niels van Oort, Delft University of Technology
Bart van Arem, Delft University of Technology