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For most ultralong bus lines, low punctuality rates and vehicle bunching are two main challenges. One potential strategy to solve these two problems is to split an ultralong line into two or three shorter lines according to historical passenger flow patterns as well as other presetting objectives and constraints. Thus, in this paper, a multiobjective optimization model is first established in consideration of both passenger flow distribution and coline metro networks. Then, the optimal truncated stop is determined by estimating the model results generated by enumerating each potential bus stop derived from related metro stops. The nondominated sorting genetic algorithm II (NSGA-II) is applied to solve the model efficiently. A case study conducted on a real bus line associated with metro networks in Beijing, China, demonstrates the efficiency of the optimization strategy adoption in terms of operating cost and passenger travel time, even with a slight increase in passenger cost. Besides, the multivehicle type is also referred in the experiments to verify economic efficiency to reduce operational costs. [ABSTRACT FROM AUTHOR]

This paper discusses the use of smart card big data to understand and evaluate the public transport network in Seoul, South Korea. Smart card data (SCD) trap multiple transit modes and cover a large population, thus providing accurate and detailed trajectory information compared with traditional survey data. The focus of this study was on understanding route choice behaviours on multi-modal routes (MMRs) by redefining the route choice problem for MMRs as a transfer subway station selection problem. The study generated accurate and realistic alternative routes by creating hyperpaths, considering overlapping bus lines and the existence of more than one bus stop around subway stations using a multinomial logit model based on 1 year of SCD. The trained models were determined to be general enough by validating them with a 6-month SCD validation set. [ABSTRACT FROM AUTHOR]

The estimation of the number of passenger boardings at every bus stop along an itinerary was solved using a Bayesian method. The method employs probability distributions of Wi-Fi probe requests. Any available technique capable of uniquely identifying probe requests made by onboard Wi-Fi devices can be used for obtaining the samples needed for the estimation. Probability distributions of time intervals between consecutive probe requests provide the information needed for generating the probability density function of boarding amounts at each stop. A case study of synthetic probe requests randomly generated for a bus line comprised of 17 stops indicated the adequacy of the method. Its efficacy increases with the grouping of nearby stops in boarding zones. It is straightforward to modify the method for the estimation of alightings, origin–destination matrixes, and bus loads. [ABSTRACT FROM AUTHOR]

Public transportation systems play a vital role in the mobility of cities and the quality of life of their residents. With the advent of GPS technology, it is now possible to collect vast amounts of data on the movements of public buses. In this study, we investigate if it is possible to use approach based on Needleman-Wunsch algorithm (NWA) for identifying bus lines from logged GPS tracks of buses based on a timetable from the year 2020 in Rio de Janeiro, Brazil. Further we discuss possible drawbacks of that approach and reasons why such approach could not be efficiently used. As alternative we introduce approach that relies solely on the order of bus stops encoded in geohash codes to identify the correct bus line of track, eliminating the inaccuracies caused by location interpolation used in NWA. Our findings suggest that approach based on NWA could not be efficiently used for labeling logged GPS bus tracks by bus lines because of interpolation and global alignment. But introduced alternative approach could reliable assign to bus tracks their bus line. Such approach is also able to filter out inaccurate bus tracks and outliers. Detecting anomalies and outliers can provide valuable insights into the transportation patterns of cities, and analyzing the data from public transportation systems can inform the development of more efficient and effective transportation policies. [ABSTRACT FROM AUTHOR]

Public transport priority is the development trend in public transport, and signal priority is its main means. In order to further improve the accuracy of delay calculation and realize the priority of bus signals, this paper proposes the idea of multiple conversion criteria and consideration of stop time for the coordination and control of bus and car mixed traffic flow trunk roads. First of all, on the basis of in-depth analysis of the differences in the characteristics of bus and car models, a multi-conversion standard delay calculation method is proposed, and its effectiveness is verified by simulation. The results show that compared with the single conversion standard delay calculation method, the average delay error of cars and buses calculated by this method is reduced by 22.54% and 82.21%, respectively. Then, the influence of bus stops on bus speed and delay is further analyzed, and the coordinated control model of bus priority trunk roads considering bus stops is constructed with the passenger capacity of each bus line and the per capita delay as the goal, and the solution is given. Finally, 178 randomly generated examples are used to verify and analyze the effectiveness and sensitivity of this model. [ABSTRACT FROM AUTHOR]

Overlapping bus lines are ubiquitous in bus networks, particularly in metropolitan areas. The overlapping of bus lines can provide convenience for passengers who wish to transfer. However, it also tends to cause bus bunching at overlapping segment stops. Moreover, overlapping of bus lines introduces additional complexity to the operation of bus systems. This study aimed to dispatch bus vehicles entering overlapping segments dynamically by adopting speed control and green light extension strategies. This ensures that transferring passengers experience less transfer waiting time and reduced bus bunching at overlapping segments. The proposed model considers environmental constraints on vehicle speed and the stochastic factors of passenger arrivals at a stop. Synchronization is maximized by controlling the speed of vehicles along a roadway and determining whether a green light extension strategy is enabled. The effectiveness of the proposed model was verified by applying it to a real overlapping segment in Harbin, China. The results demonstrate that the proposed model can more than double the opportunity for synchronization in overlapping segments while reducing bus bunching at the stops in overlapping segments. [ABSTRACT FROM AUTHOR]

Background: Since December 2019, COVID-19 began to spread throughout the world for nearly two years. During the epidemic, the travel intensity of most urban residents has dropped significantly, and they can only complete inflexible travel such as "home to designated hospital" and "home to supermarket" and some special commuting trips. While ensuring basic travel of residents under major public health emergency, there is also a problem of high risk of infection caused by exposure of the population to the public transport network. For the discipline of urban transport, how to use planning methods to promote public health and reduce the potential spread of diseases has become a common problem faced by the government, academia and industry. Method: Based on the mobility perspective of travel agents, the spatial analysis methods such as topological model of bus network structure, centrality model of public transport network and nuclear density analysis are used to obtain the exposure risk and spatial distribution characteristics of public transport from two aspects of bus stops and epidemic sites. Results: The overall spatial exposure risk of Wuhan city presents an obvious "multi center circle" structure at the level of bus stops. The high and relatively high risk stops are mainly transport hubs, shopping malls and other sites, accounting for 35.63%. The medium and low-risk stops are mainly the villages and communities outside the core areas of each administrative region, accounting for 64.37%. On the other hand, at the scale of epidemic sites, the coverage covers 4018 bus stops in Wuhan, accounting for 36.5% of all bus stops, and 169 bus lines, accounting for 39.9% of all routes. High risk epidemic sites are mainly concentrated in the core areas within the jurisdiction of Wuhan City, and in the direction of urban outer circle diffusion, they are mainly distributed in the low and medium risk epidemic sites. According to the difference of the risk level of public transport exposure, the hierarchical public transport control measures are formulated. Discussion: This paper proposes differentiated prevention and control countermeasures according to the difference of risk levels, and provides theoretical basis and decision-making reference for urban traffic management departments in emergency management and formulation of prevention and control countermeasures. [ABSTRACT FROM AUTHOR]

• A joint optimization model for bus timetable and seat allocation has been developed. • The different capacity vehicles collaboration scheduling strategy is proposed. • The utilization of bus capacity is improved and the bus operating cost is reduced. • Several numerical experiments on Beijing bus line show the effectiveness of the proposed approaches. Reservation-based travel is an effective method for managing passenger demand during public health emergency. It helps limit the number of passengers boarding at each bus stop, thereby reducing the risk of infectious disease transmission among passengers. This study proposes a joint optimization method for bus scheduling and seat allocation with the consideration of travel time stochasticity based on the reservation-based travel, in which different buses with various capacities are put into operations to satisfy the uneven passenger demand in different periods. To characterize the problem mathematically, a mixed-integer nonlinear programming model is formulated to simultaneously minimize the passenger waiting cost and operating costs of the bus system. This model is further reformulated equivalently into a mixed-integer linear programming model via the linearization method. An effective heuristic algorithm is designed to find high-quality solution for the proposed model. Finally, two sets of numerical examples, including a small-scale example and a large-scale example based on the Beijing bus line 6, are conducted to validate the performance of the proposed method. The experimental results demonstrate that the proposed method can effectively reduce operation costs and satisfy passenger demand during different periods. [ABSTRACT FROM AUTHOR]

This study integrates the fields of geography, urban transit planning, and statistical learning to develop a sophisticated methodology for predicting bus demand at the stop level. It uses a Generalized Additive Model that captures non-linear relationships and incorporates spatial dependence, improving traditional methods. It showcases a high predictive capacity with a pseudo R-squared of 0.79 during its validation, ensuring substantial explanatory power for new observations. A large number of variables, including land-use characteristics, socioeconomic factors, and transit supply, are analysed. These widely available predictors facilitate the transferability of the methodology to other urban areas. Transit supply predictor considers the number of annual trips per stop and area as well as the location of stops along the lines that serve them. GIS processing of the data allows the calculation of variables within the areas of influence of each stop, obtained by following the walkable street network. For the case study, the presence of universities, hospitals, and lodgings areas, as well as inhabitants and ratio of bus trips show a positive impact on bus demand. This geo-analysis process employs accurate disaggregated data, such as information on uses in each building, as well as methods for assigning socioeconomic information from local areas to residential buildings. This study highlights the complex relationship between the location of transit network stops, both along the bus line and in terms of geographical proximity, their transit supply, and its surrounding factors. The results indicate that there is spatial dependence for stops less than 1.15 km apart. The developed methodology provides reliable information to transit network planners for decision making. Specifically, this proposed methodology can contribute to designing new routes, optimizing stop locations, and estimating the impact of changes in the transit network or urban planning on bus demand. All these improvement measures promote sustainable urban mobility, consequently fostering environmental and social benefits. • Integration of geography, transit planning, and statistics in bus demand prediction. • Novel statistical approach with non-linear relationships and spatial dependence. • Detailed GIS-based analysis, cleaning and processing of open data. • Bus supply, socioeconomic data and land use analysis within stops' influence area. • Consideration of actual walking distances from stops using walkable street networks. [ABSTRACT FROM AUTHOR]

Evaluating the reliability of a bus line can provide useful information on the service performance of the city's bus transit system and the need for improvements. This paper presents a reliability evaluation model of a bus line based on information entropy. The model integrates the weighted bus-stop index and the variation index of bus travel time. To better determine the weights of the bus stop, six indices were established based on three characteristics: passenger flow, bus-stop properties, and the environment. The proposed model was applied to typical bus lines in Fuzhou, People's Republic of China. The results show that most bus-stop reliabilities at the peak period were smaller than those at the off-peak period. In addition, for either peak or off-peak period, the reliability shows a slight upward trend following a gradual decline after the first stop, generally reflecting the influence of passenger flow, environment, and location factors. [ABSTRACT FROM AUTHOR]

Airport buses play a crucial role in addressing the last-mile problem of air travel, especially in cities and countries lacking inner-city rail transit systems. Nevertheless, airport buses are currently witnessing a decline in ridership due to drawbacks such as long departure intervals, inflexible stops, and considerable distances between stops. Consequently, delivering high-quality airport bus services has become a pressing concern for public transport operators. Motivated by new flexible buses and ride-sharing services, this paper explores a flexible airport bus service that integrates ride-sharing services for passengers traveling from bus stops to their destinations. This problem entails integrated decisions involving bus stop selection, passenger assignment to drop-off bus stops, as well as bus and ride-sharing routing. Accordingly, this problem presents more challenges in decision-making than traditional flexible bus or ride-sharing routing problems. We first develop an arc-based mixed-integer linear programming model. Subsequently, we design a double decomposition math-heuristic algorithm that builds upon logic-based Benders decomposition and column generation algorithms to obtain a near-optimal solution within practical computation time limits for practical-scale instances. Additionally, we implement an adaptive large neighborhood search algorithm to evaluate the solution quality of this math-heuristic algorithm and to solve large-scale instances. To validate the effectiveness of both the model and the algorithms, we conduct numerical experiments using instances derived from Shenzhen airport bus lines. The experimental results demonstrate that the flexible service mode offers significant advantages in reducing both passenger ride time and vehicle mileage over traditional airport bus or taxi modes. • The flexible airport bus problem with last-mile ride-sharing services is introduced. • An arc-based mixed-integer programming model is proposed. • A logic-based Benders decomposition math-heuristic algorithm is designed. • An adaptive large neighborhood search algorithm is developed. • Numerical experiments using instances derived from Shenzhen airport are conducted. [ABSTRACT FROM AUTHOR]

• Bus network design without a predefined pool line. • Realistic consideration of the transfer time. • Bimodal pedestrian-bus network. • Incorporation of passenger assignment. We face the problem of designing a bus transit network, locating the most convenient bus stops and defining the set of bus lines without considering an a priori set of candidate lines. In order to model the problem, we consider a bimodal network composed of two layers, each one corresponding to a different transportation mode: pedestrian and bus. We propose two exact formulations and compare them by means of the ϵ -constraint method, thus obtaining a set of efficient solutions that simultaneously consider the total user travel time (composed of in-bus travel time, waiting time, walking time and transfer time) and service operator objectives. Finally, the best formulation is tested in a real case scenario. [ABSTRACT FROM AUTHOR]

The uneven distribution of travel demand is incredibly commonplace in cities, but insufficient attention has been paid to this problem. In this paper, we explore the impact of the uneven distribution of travel demand on an accessible network. A model with a sparse and dense mixed grid transit network based on an uneven distribution of travel demand is proposed to provide a high-performance bus service. The transit network was composed of two parts: a dense grid network in the downtown area and a sparse grid network in the periphery. The objective function of the model included agency cost and passenger cost, where the decision variables were the downtown-to-city ratio, the downtown headway, stop spacing (line spacing), and ratio of the periphery headway to the downtown headway. This study validated the proposed model using the demands of San Francisco. The concentrated spatial demand resulted in a lower total cost, whereas the varying travel demand must be controlled within an appropriate range to maintain the bus performance. The stable bus lines and stops with a variable timetable of the proposed model are profitable for fast-growing cities. [ABSTRACT FROM AUTHOR]

We are witnessing a rapid growth of electrified vehicles due to the ever-increasing concerns on urban air quality and energy security. Compared to other types of electric vehicles, electric buses have not yet been prevailingly adopted worldwide due to their high owning and operating costs, long charging time, and the uneven spatial distribution of charging facilities. Moreover, the highly dynamic environment factors such as unpredictable traffic congestion, different passenger demands, and even the changing weather can significantly affect electric bus charging efficiency and potentially hinder the further promotion of large-scale electric bus fleets. To address these issues, in this article, we first analyze a real-world dataset including massive data from 16,359 electric buses, 1,400 bus lines, and 5,562 bus stops. Then, we investigate the electric bus network to understand its operating and charging patterns, and further verify the necessity and feasibility of a real-time charging scheduling. With such understanding, we design busCharging, a pricing-aware real-time charging scheduling system based on Markov Decision Process to reduce the overall charging and operating costs for city-scale electric bus fleets, taking the time-variant electricity pricing into account. To show the effectiveness of busCharging, we implement it with the real-world data from Shenzhen, which includes GPS data of electric buses, the metadata of all bus lines and bus stops, combined with data of 376 charging stations for electric buses. The evaluation results show that busCharging dramatically reduces the charging cost by 23.7% and 12.8% of electricity usage simultaneously. Finally, we design a scheduling-based charging station expansion strategy to verify our busCharging is also effective during the charging station expansion process. [ABSTRACT FROM AUTHOR]

The planning and operation of urban buses depend heavily on the time-varying origin-destination (OD) matrix for bus passengers. In most cities, however, only boarding information is recorded, while the alighting information is not available. This paper proposes a novel method to predict the destination of a single bus passenger based on bus smartcard data, metro smartcard data, and global positioning system (GPS) bus data. First, the attractiveness of each bus stop in a bus line was evaluated, considering the attractiveness of nearby metro stations. Then, the exploration and preferential return (EPR) model was employed to estimate the probability of a bus stop to be the alighting stop, i.e., the destination, of a passenger. The estimation result was obtained through a simulation based on the Monte Carlo (MC) algorithm. The effectiveness of our method was proved through a case study on the bus network in Shenzhen, China. [ABSTRACT FROM AUTHOR]

In this study, we demonstrate the development of a methodology for simulating transit bus ridership and greenhouse gas emissions (in CO2 equivalent) across a network of 200 bus lines in the city of Montreal, Canada. The current study estimates ridership and emissions at a micro level, that is, at the disaggregate stop level for 24 h of a typical weekday for all the bus lines (and their trips) in the bus network of Montreal. The disaggregate level simulation process incorporates for specificities such as vehicle type, age, fuel, and passenger load. The micro-simulation platform developed has three embedded modules: (1) stop level boarding and alighting, (2) bus occupancy determination, and (3) disaggregate emission estimation. Our simulation allows us to estimate emissions for individual buses while running as well as while idling at bus stops. The proposed model system provides measures of per capita emissions (total emissions/total boardings), per km emissions (total emissions/total kilometers traveled), and per person km emissions (total emissions/total person kilometers traveled on bus transit). To further illustrate the applicability of the proposed study, we conducted several policy scenario analyses. We investigated the effect of a 20% system-wide increase in ridership and observed a 1.1% increase in total emissions and a 13% decrease in per capita emissions. We estimated the effects of decreasing the frequency of low occupancy buses and increasing the frequency of high occupancy buses as well and observed that these frequency changes are associated with proportional changes in emissions. [ABSTRACT FROM AUTHOR]

Transit ridership modeling at the bus stop level is an important tool for bus network planning and transit-oriented development. However, many cities, especially in developing countries, face a lack of boarding and alighting data due to the high costs of collection. Solutions based on smartcards often rely on assumptions that negatively affect the data accuracy. Noting that previous studies suggest the existence of spatial heterogeneity and dependence in factors affecting stop-level ridership, the present paper proposes the application of Geographically Weighted Negative Binomial Regression (GWNBR) to modeling the transit ridership along bus lines in São Paulo – SP (Brazil) under missing data conditions. Four important topics are analyzed: 1) whether the spatial variation of predictors' effects is statistically significant; 2) the consistency of parameter estimates; 3) the prediction power sensitivity to missing data scenarios; and 4) the use of network distances replacing the traditional Euclidean ones. Of the five predictors that explained the transit ridership better, overlapping and frequency proved to have coefficients with statistically significant spatial variation. Goodness-of-fit measures indicated that GWNBR is an effective tool to the transit ridership estimation in uncounted bus stops, even when the availability of data is low. GWNBR in missing data scenarios could, in fact, reproduce the spatial pattern of effects shown in the complete database model, for some explanatory variables. Network distances may better represent the spatial relationship between transit ridership and some of its predictors. In addition, GWNBR models were able to address the spatial dependence found in the Negative Binomial Regression. [ABSTRACT FROM AUTHOR]

Transit management agencies often pre-allocate a multi-berth stop's berths to specific bus lines so that passengers can find the right place to wait for their buses. Developing optimal berth allocation plans that minimize the total bus delay is essential for mitigating bus queues at busy multi-berth stops. However, this problem is challenging due to the huge solution space, the high degree of stochasticity in bus queues, and the resulting extremely high computational cost. In this paper, we first propose a simple heuristic method inspired by queueing theory. It is based on the idea that evenly distributing the total traffic intensity (defined as the total bus arrival rate times the mean dwell time) among all the berths would produce a lower bus delay. Numerical results demonstrate that this simple method generated very good berth allocation plans (with optimality gaps < 6 % for no-overtaking and free-overtaking stops) in seconds! It does not rely on time-consuming simulation surrogate models or numerous input data such as the stochastic bus arrival processes and dwell time distributions of each bus line. To further improve the simple heuristic's performance (especially for limited-overtaking stops), we develop a cluster-based nested partition algorithm that can find a near-optimal plan (e.g., with an optimality gap of < 3 %) in a much shorter time than a previous algorithm. The algorithm employs the simple heuristic plan as the initial solution. Our methods can be applied to stops with various berth numbers, different proximities to nearby traffic signals, and under diverse bus queueing rules. • Devised smart heuristics to optimize line-to-berth allocation, reducing bus delays. • Proposed a simple heuristic evenly distributing bus traffic intensity among berths. • The simple heuristic offers near-optimal allocation and can be derived in seconds. • A cluster-based nested partition algorithm further improves the simple heuristic. • Our methods suit stops with varied berth numbers, signal proximities and queue rules. [ABSTRACT FROM AUTHOR]

With the increase of buses and bus lines, a negative effect on road section capacity is made by the prolonged delay and queuing time at bus stops. However, existing methods of measuring the negative effect pay little attention to different bus stop types in the curb lanes. This paper uses Gap theory and Queuing theory to build models for effect-time and potential capacity in different conditions, including curbside bus stops, bus bays with overflow and bus bays without overflow. In order to make the effect-time models accurate and reliable, two types of probabilities are introduced. One is the probability that the dwell time is less than the headway of curb lane at curbside bus stops; the other is the overflow probability at bus bays. Based on the fundamental road capacity model and effect-time models, potential capacity models of curb lane are designed. The new models are calibrated by the survey data from Changchun City, and verified by the simulation software of VISSIM. Furthermore, with different arrival rates of vehicles, the setting conditions of bus stops are researched. Results show that the potential capacity models have high precision. They can offer a reference for recognizing the effect of bus stops on the capacity of curb lane, which can provide a basis for planning, design and management of urban roads and bus stops. [ABSTRACT FROM AUTHOR]

This paper models part of a public transport network (PTN), specifically, a bus route, as a small-size multi-agent system (MAS). The proposed approach is applied to a case study considering a ‘real world’ bus line within the PTN in Auckland, New Zealand. The MAS-based analysis uses modeling and simulation to examine the characteristics of the observed system – autonomous agents interacting with one another – under different scenarios, considering bus capacity and frequency of service for existing and projected public transport (PT) demand. A simulation model of a bus route is developed, calibrated and validated. Several results are attained, such as when the PT passenger load is not close to bus capacity, this load has no effect on average passenger waiting time at bus stops. The model proposed can be useful to practitioners as a tool to model the interaction between buses and other agents. [ABSTRACT FROM AUTHOR]

An algorithm for automated generating of itinerary between two randomly chosen bus stops has been described in this article. The itinerary may be generated according to the time of departure or according to the desired time of arrival at a certain bus stop. [ABSTRACT FROM AUTHOR]

The holding strategy performs well in resisting bus bunching in the dedicated bus route. Yet, its efficiency in a non-dedicated bus route has not been well studied, and its external effects on other road participants are unclear. We study this topic in a circular two-lane traffic system consisting of buses and cars. A cellular automaton model is used to depict the vehicles' dynamic behaviors. By numerical simulations, we obtained the phase diagram of the bus system, and identified the four states concerning the stability and level of service. Then we analyzed the flow and velocity characteristics and distinguished three states of the traffic system. Our simulations prove the separation effect of car flows on the bus headway regulation. Combined with this effect, the holding strategy makes the bus running more homogeneous and the bus utilization more evenly in the mixed traffic flow condition. Interestingly, from a macro perspective, the holding strategy incurs no detrimental effects on the whole traffic system, though it might intensify local interference between buses and cars near bus stops. Under certain conditions, the average speed of buses and cars increases, eventually increasing the system's capacity. • Interference between buses and cars in mixed traffic flow conditions is analyzed. • Bus operating states with and without the holding are investigated. • The bus line characters and traffic conditions determine the holding effeteness. • External effects of the bus holdings on the car flows are discussed. [ABSTRACT FROM AUTHOR]

Bus provides eco-friendly, cheap, convenient, time-effective means of transportation. Bus lack regularization and management, due to which they increased the burden on public transportation system. Unusual and unexpected conditions on the roads affect the smooth operation of the bus system and the movement of vehicles. Problems faced by passengers such as traffic congestion, unexpected delays of buses, unorganised bus dispatching from depots takes place as a result of which passengers have to suffer a lot and they inevitably have to wait for arrival of buses. In present time, everyone is in hurry to reach their destination. In this situation, waiting for the buses in queue is not reliable. People who completely rely on the bus transportation, their major concern is to know the real time location of the bus, the time it will take to reach their bus stop, the information about the driver and the security. This information helps people in making better travelling decisions. This RFID-Based Bus Management System is useful, accurate, secure and flexible. In this Global Positioning System (GPS) and Radio Frequency Identification Tags (RFID) is used for obtaining the location of bus and information about passengers and driver. The paper aims at developing a real-time RFID-Based Bus Management System which focuses on providing convenience and safety to the passengers. The system will be working as an integrated function of RFID and GPS which will maintain the detailed information about passengers and bus. The main objective of this system is to develop flexible, user friendly and secure bus management system that caters the needs of passengers. [ABSTRACT FROM AUTHOR]

At some location buses stop to pick up the passengers waiting there and leave immediately, empty or occupied. Passenger arrival times as well as bus arrival times form independent renewal processes. Every passenger is willing to wait for a random amount of time before leaving, and every bus takes away all waiting passengers. For this pickup model, we study the distributions of the number of waiting passengers and of the individual sojourn times. The sojourn times lead to a Markov chain embedded in the superposition of the two underlying renewal arrival processes, for which we study its convergence toward stationarity. [ABSTRACT FROM AUTHOR]

Transit stop spacing is an important indicator in deploying public transit services. This paper presents a hierarchical process for optimizing distribution of bus stops in the context of multi-modal transit development in large cities. Firstly, connection stops are generated manually to connect with other transit facilities. Secondly, key stops are identified based on a coverage model that minimizing total distance, using node centrality value as weight. Thirdly, ordinary bus stops are optimized with coverage model that maximizing potential transit demand. The analytical process is based on a combination of raster and vector data models in Geographical information system, which allows for flexible computing and effective evaluation. [ABSTRACT FROM AUTHOR]

Abstract: Bus stops are often placed short distances upstream of signalized intersections. Buses that dwell at one of these so-called near-side stops can impede queued cars upstream from discharging through the intersection during green times. Residual car queues can form at the intersection as a result. The smaller the distance between a stop and its intersection, the greater the problem can be. Models are formulated to address this problem using kinematic wave theory. The models can be used to determine where to place a near-side stop to achieve a target level of residual car queueing. In addition, the models are used herein to develop a scheme for mitigating residual car queues by briefly detaining some buses from reaching the stop. The scheme can be applied selectively, so that the times that detained buses depart from the stop are not postponed. The buses are therefore not delayed over the longer run. Analysis indicates that this bus-holding scheme can significantly reduce car delays and queueing. Our models for placing stops and holding buses are shown to be robust to systematic and random changes in car flow. [Copyright &y& Elsevier]

The article analyses optimal bus stop locations under different network congestion levels applying a bi-level optimisation model, covering an upper level minimizing an overall cost function (Social Cost) and a lower level that includes a modal split assignment model. This model is applied to Santander city (Spain) under a range of demand levels, starting from very low to high congestion, representing the evolution of variables in each case and analysing different solutions. The optimal distances between stops obtained for each demand and congestion level indicate that very low demands produce wider spaces. However, as demand increases, accessibility to public transport service should be increased and then spacing between bus stops drops to 360 metres. [ABSTRACT FROM AUTHOR]

Abstract: Bus schedules cannot be easily maintained on busy lines with short headways: experience shows that buses offering this type of service usually arrive irregularly at their stops, often in bunches. Although transit agencies build slack into their schedules to alleviate this problem – if necessary holding buses at control points to stay on schedule – their attempts often fail because practical amounts of slack cannot prevent large localized disruptions from spreading system-wide. This paper systematically analyzes an adaptive control scheme to mitigate this problem. The proposed scheme dynamically determines bus holding times at a route’s control points based on real-time headway information. The method requires less slack than the conventional, schedule-based approach to produce headways within a given tolerance. This allows buses to travel faster than with the conventional approach, reducing in-vehicle passenger delay and increasing bus productivity. [Copyright &y& Elsevier]

In this paper, we use the traffic flow model proposed by Tang et al. [Physica A387, 6845 (2008)] to study the effects of bus stop on traffic flow. Our numerical tests show that bus stop will have great effects on the stability of traffic flow and that the effects are related to the initial density and the number of bus stops. The numerical results are accordant with the real traffic, which shows that the model proposed by Tang et al. can describe some complex traffic phenomena resulted by bus stop. [ABSTRACT FROM AUTHOR]

Over the last years, there have been initiated several pilots with autonomous minibusses. Unlike regular bus services, autonomous minibusses serve a limited number of stops and have more flexible schedules since they do not require bus drivers. This allows the operation of a line through a flexible combination of sublines , where a subline serves a subset of consecutive stops in the same order as the original line. This paper studies the subline frequency setting (SFS) problem under uncertain passenger demand. We present a frequency setting model that assigns autonomous minibusses to sublines in order to exploit the available resources as much as possible and minimize the operational and passenger waiting time costs. Passenger waiting time costs may depend on the combination of several lines whose frequencies cannot be perfectly aligned for each passenger journey. We present a new estimation of the expected waiting time for passengers to improve the accuracy of the passenger waiting time costs in the case of sublines. Our SFS model is originally formulated as a MINLP and reformulated as a MILP that can be solved to global optimality. Further, we explicitly consider the uncertainty of passenger demand in the optimization process by formulating a stochastic optimization model. The performances of our stochastic and deterministic models that assign minibusses to sublines are tested under various passenger demand scenarios in the 14-stop autonomous minibus line in Eberbach, Germany and a fictional bus line with 20 bus stops. Results show potential improvements in operational costs in the range of 10%–40% depending on the passenger demand profile. • Novel MILP formulation of the subline frequency setting (SFS) problem. • New estimation of the expected passenger waiting time in the case of sublines. • Incorporation of passenger demand uncertainty in the optimization process. • Improvements in operational costs of up to 40% • Exact solutions for lines of up to 25 stops per direction in less than 7 h. [ABSTRACT FROM AUTHOR]

Abstract: The aim of this research is to contribute to the development of public transport services for a heavily car-dependent society. The paper investigates, through a set of actual empirical data, an optimal structure to improve the bus system and contribute to the sustainable development of the city of Riyadh, Kingdom of Saudi Arabia. The analysis is based on a computer FORTRAN program to simulate the optimum bus stop spacing based on the field-collected data. It concludes that many of the requirements of an adequate bus system might be provided by appropriate bus stops and that these should be incorporated as part of the bus priority measures in the City of Riyadh. [Copyright &y& Elsevier]

In this paper, we undertake a detailed study of the bus stop problem in congested transit networks. In the first part of the paper, we present and discuss the bus stop models existing in the literature. In the second part, we propose a new general model for which we prove a number of good properties and we give equivalent formulations. Then, we examine two special cases of the general model. In the first case, the line capacities are considered limited and therefore they can not be exceeded by the on-board passenger flows. In the second case, the strict capacity constraints are relaxed in order to obtain a stop model that can be easily integrated into an assignment model to predict the global passenger behavior in transit networks. [ABSTRACT FROM AUTHOR]

It is well known that if passengers arrive at a bus stop at random, the average time they have to wait before a bus comes is given by a formulae. However, if there is a published timetable for the service, or if buses tend to run at certain fixed times rather than at others and there are some passengers who frequently make the same, suggest a further improvement. Even at very high average headways, there could not be much reduction of waiting time if the buses were very unreliable, whereas at moderate headways there are considerable potential savings if the buses are very reliable. Consequently, one expect some measure of the day-to-day predictability of the bus service, such as the standard deviation of times of individual buses, to be more meaningfully related to actual average passenger waiting time than is or brand. This approach implies a need for data on bus timings to be collected at the same period of the day repeatedly on several different days, and this article reports on the observation of this kind made in suburban London in the summer of 1974.

Describes a method for the neurosimulation modeling of a scheduled bus route. Overview of bus operations; Simulation model; Neural network; Speed regulation; Delays along bus route.

The article discusses on the bus stop as an urban place. Bus stops are said to advertise the transit system to the public. According to the article, they reflects a city's public space. It is also a place where the identity of the municipality and bus transit overlap. Several bus stops situated in the different cities of the United States are also featured. A bus stop in Denver, Colorado's transit mall has movable chairs where passengers can make their casual seating. The presence of the movable chairs makes the waiting for the bus almost leisurely to the people. Meanwhile, passengers in bus stops in Los Angeles, California wait on advertising benches.

As prediction of trip-based electricity consumption has become an prerequisite for the deployment of large-scale EB fleets, this study has established random forest-based models to systematically investigate the impacts of environmental conditions, route characteristics, and dynamic traffic conditions. The models have been performed on real-world data collected from 1024 EBs over five consecutive months in Shenzhen, China. The results show that considering all the influencing variables can significantly enhance the prediction performance, but comparatively speaking, the route characteristics contribute the most among the three categories and involving more variables demonstrates greater advantages within the trip length under 20 km. It is also found that the trip length, the number of bus stops and the number of the traffic lights passed rank the top three most influencing factors, while the wet-dry condition is the least one. In addition, the variations under five operation scenarios show similar trend. The trip length and average travel speed are inversely proportional to the specific electricity consumption, while the number of bus stops visited, traffic lights passed, and ambient temperature exhibit a gentle proportional relationship. Moreover, it is suggested to plan the new bus line over 10 km in terms of reducing electricity consumption per kilometre. • Five random forest-based prediction models are built for performance comparsion. • Impacts of three categories of variables on EB electricity consumption are explored. • The prediction models perform on big data collected from 1024 EBs over five months. • A variable importance analysis is conducted to rank all the nine variables. • Local sensitivity analysis is conducted under five operation scenarios. [ABSTRACT FROM AUTHOR]

The main goal of this article is to evaluate the relationship between the spatial distribution of the population of the city of Belo Horizonte, one of the largest cities in South America, and its public transport network, seeking to ascertain whether the current bus network is able to meet the needs of the population. The methodology was based on data processing and vector information from different sources through spatial analysis tools. As expected, the results revealed a greater concentration of routes and bus stops in the central region of Belo Horizonte, which is the main commercial center of the municipality and its metropolitan region. However, the results suggest evidences of a disadvantage for the population with lower income, especially those living on the periphery. A possible explanation for this disadvantage is the invisibility of these communities due to their high level of irregular occupation. Under the eyes of public transport planning, they are not recognized, and, theoretically, do not generate demand for lines and bus stops [ABSTRACT FROM AUTHOR]

• Analytic solution for the single variable bus holding problem considering capacity limits. • The holding time of a vehicle can be determined in real time. • Significant reduction of stranded passengers in the expense of a slight regularity deterioration. • The service regularity performance is relatively insensitive to demand variations. This study focuses on single variable optimization approaches which determine the holding time of a vehicle when it is ready to depart from a bus stop. Up to now, single variable optimization methods resort to rule-based control logics to equalize the inter-departure headways or adhere to the target headway values. One of them is the two-headway-based control logic which determines the holding time of a bus based on its headway with its preceding and following bus without addressing other implications, such as overcrowding. To rectify this, we introduce a new model for the single variable bus holding problem that considers the passenger demand and vehicle capacity limits. Then, we reformulate this problem to an easier-to-solve program with the use of slack variables and introduce an analytic solution that can determine the holding time of a vehicle at the respective bus stop. Our analytic solution does not add a computational burden to the two-headway-based control logic and can be applied in real time. The operational benefit of our bus holding approach compared to other analytic solutions that do not consider the vehicle capacity is investigated using actual data from bus line 302 in Singapore. [ABSTRACT FROM AUTHOR]

Lifetime cancer risk and exposure of daily commuters to polycyclic aromatic hydrocarbons (PAHs) in cities of Northwest China were determined from a study of street dust samples obtained from bus stops in Qingyang city. The sum of 16 priority PAHs (Σ16 PAHs) concentrations in the dust samples ranged from 0.8 to 18.3 mg kg−1 (mean 3.0 mg kg−1) and the distribution of individual, carcinogenic, combustion specific, low (2–3 rings) and high molecular weight (4–6 rings) PAHs was determined. The benzo[ a ]pyrene toxic equivalents of Σ16 PAHs ranged from 0.01 to 12.2 mg kg−1 (mean 0.8 mg kg−1). Incremental lifetime cancer risk from exposure to PAHs in dust at bus stops in Qingyang city was estimated at 1.9 × 10−6 for adults and 3.5 × 10−6 for children (confidence limit ≥ 95%). Emission source analysis of PAHs in bus stop dust showed that they were mainly derived from residential coal, oil and biomass combustion, e.g. from boilers, traffic vehicles, and Kang heaters. Higher concentrations of PAHs were obtained at bus stops near transport hubs, commercial districts, and administrative institutions. Image 1 • Health risk to daily commuters from PAHs in dust at most bus stops in Qingyang was moderate. • The results deserve the attention of researchers and governments in other cities. • The government should try to reduce congestion of vehicle and population flow. • The government should reasonably set up the number of bus lines and bus stops. [ABSTRACT FROM AUTHOR]

Customized Bus is a new mode of Internet-supported public transportation. It is regarded as one of the major strategies to reduce the usage of private cars and mitigate greenhouse gas emissions from road traffic. For a customized bus system, a dynamic bus line planning system based on the demand can largely improve the performance and promote the public acceptance of customized bus service. This paper introduces a method to generate planning suggestions for bus lines and stops based on massive demand data. A link network is generated from the input to represent the sharing route of the demand. With community detection, the link network is segmented into communities with similar travel routes. By examining the core-peripheral structure and matching the core part of communities with the road network, the customized bus lines are generated. Boarding and alighting hotspots are identified as the suggestion for customized bus stops. The methodology is tested by using mobile phone data in Tokyo. With the input of one-day sample, the algorithm can generate the result in approximately 1 min and extract 29 bus lines. According to the shape and spatial location of the bus lines, three types of bus lines serving different travel patterns are classified: radiation type lines, ring-type lines, and suburban lines. Analyzing the emission reduction potential of the extracted bus lines manifestes that bus line planning of the proposed method has the potential to relieve emission pressure on urban expressways and to reduce approximately 13% of road traffic emission. Image 1 • A data-driven approach is proposed for the systematic planning of customized bus. • 1.4 million car trajectories are used to simulate dynamic input for the method. • Dynamic bus lines are generated from spatial distribution of sharing trajectories. • Suggestion of bus stops are extracted from boarding and alighting hotspots. • Potential demand and emission reduction of customized buses is analyzed. [ABSTRACT FROM AUTHOR]