An improved headway-based holding strategy for bus transit

Abstract

This paper presents an improved headway-based holding strategy integrating bus transit travel and dwelling time prediction. A support vector machine-based (SVM) model is developed to predict the baseline travel and dwell times of buses based on recent data. In order to reduce prediction errors, an adaptive algorithm is used together with real-time bus operational information and estimated baseline times from SVM models. The objective of the improved holding strategy is to minimize the total waiting times of passengers at the current stop and at successive stops. Considering the time-varying features of bus running, a ‘forgetting factor’ is introduced to weight the most recent data and reduce the disturbance from unexpected incidents. Finally, the improved holding strategy proposed in this study is illustrated using the microscopic simulation model Paramics and some conclusions are drawn.     

Integrated optimization of bus priority operations in connected vehicle environment

Most previous works associated with transit signal priority merely focus on the optimization of signal timings, ignoring both bus speed and dwell time at bus stops. This paper presents a novel approach to optimize the holding time at bus stops, signal timings, and bus speed to provide priority to buses at isolated intersections. The objective of the proposed model is to minimize the weighted average vehicle delays of the intersection, which includes both bus delay and impact on nearby intersection traffic, ensuring that buses clear these intersections without being stopped by a red light. A set of formulations are developed to explicitly capture the interaction between bus speed, bus holding time, and transit priority signal timings. Experimental analysis is used to show that the proposed model has minimal negative impacts on general traffic and outperforms the no priority, signal priority only, and signal priority with holding control strategies (no bus speed adjustment) in terms of reducing average bus delays and stops. A sensitivity analysis further demonstrates the potential of the proposed approach to be applied to bus priority control systems in real‐time under different traffic demands, bus stop locations, and maximum speed limits. Copyright © 2016 John Wiley & Sons, Ltd.     

Real-time bus route state forecasting using particle filter and mesoscopic modeling

In the absence of system control strategies, it is common to observe bus bunching in transit operations. A transit operator would benefit from an accurate forecast of bus operations in order to control the system before it becomes too disrupted to be restored to a stable condition. To accomplish this, we present a general bus prediction framework. This framework relies on a stochastic and event-based bus operation model that provides sets of possible bus trajectories based on the observation of current bus positions, available via global positioning system (GPS) data. The median of the set of possible trajectories, called a particle, is used as the prediction. In particular, this enables the anticipation of irregularities between buses. Several bus models are proposed depending on the dwell and inter-stop running time representations. These models are calibrated and applied to a real case study thanks to the high quality data provided by TriMet (the Portland, Oregon, USA transit district). Predictions are finally evaluated by an a posteriori comparison with the real trajectories. The results highlight that only bus models accounting for the bus load can provide valid forecasts of a bus route over a large prediction horizon, especially for headway variations. Accounting for traffic signal timings and actual traffic flows does not significantly improves the prediction. Such a framework paves the way for further development of refined dynamic control strategies for bus operations.     

Crowding in public transport systems: Effects on users,operation and implications for the estimation of demand

The effects of high passenger density at bus stops, at rail stations, inside buses and trains are diverse. This paper examines the multiple dimensions of passenger crowding related to public transport demand, supply and operations, including effects on operating speed, waiting time, travel time reliability, passengers’ wellbeing, valuation of waiting and in-vehicle time savings, route and bus choice, and optimal levels of frequency, vehicle size and fare. Secondly, crowding externalities are estimated for rail and bus services in Sydney, in order to show the impact of crowding on the estimated value of in-vehicle time savings and demand prediction. Using Multinomial Logit (MNL) and Error Components (EC) models, we show that alternative assumptions concerning the threshold load factor that triggers a crowding externality effect do have an influence on the value of travel time (VTTS) for low occupancy levels (all passengers sitting); however, for high occupancy levels, alternative crowding models estimate similar VTTS. Importantly, if demand for a public transport service is estimated without explicit consideration of crowding as a source of disutility for passengers, demand will be overestimated if the service is designed to have a number of standees beyond a threshold, as analytically shown using a MNL choice model. More research is needed to explore if these findings hold with more complex choice models and in other contexts.     

Simulation of fixed route bus travel time

Travel time on fixed route urban bus route is discussed. Given that the travel time is a function of three basic variables, Monte Carlo procedure is used to simulate trips during a specific time interval. Each variable is assumed to have a specific probability distribution with known or estimatable parameters. It is shown that this micro-computer simulation model can be used for examining the effects of traffic management schemes, number of stops and passenger demand on travel time, and subsequently fleet size and level of service.     

Berth assignment planning for multi‐line bus stops

In this paper, we study an important problem that arises with the fast development of public transportation systems: when a large number of bus lines share the same bus stop, a long queue of buses often forms when they wait to get into the stop in rush hours. This causes a significant increase of bus delay and a notable drop of traffic capacity near the bus stop. Various measures had been proposed to relieve the congestions near bus stops. However, all of them require considerable financial budgets and construction time costs. In this paper, with the concept of berth assignment redesign, a simulation‐based heuristic algorithm is proposed to make full use of exiting bus berths. In this study, a trustable simulation platform is designed, and the major influencing factors for bus stop operations are considered. The concept of risk control is also introduced to better evaluate the performance of different berth arrangement plans and makes an appropriate trade‐off between the system's efficiency and stability. Finally, a heuristic algorithm is proposed to find a sub‐optimal berth assignment plan. Tests on a typical bus stop show that this algorithm is efficient and fast. The sub‐optimal berth assignment plan obtained by this algorithm could make remarkable improvements to an actual bus stop. Copyright © 2013 John Wiley & Sons, Ltd.     

Comparing static and dynamic threshold based control strategies

This research extends a static threshold based control strategy used to control headway variation to a dynamic threshold based control strategy. In the static strategy, buses are controlled by setting a threshold value that holds buses at a control point for a certain amount of time before allowing the bus to continue along the route. The threshold remains constant each time the bus stops at the control point. The dynamic strategy involves the same principle of holding buses at a bus stop; however, a different threshold value is chosen each time the bus holds at a control point. The results indicate that in cases where the static threshold is set equal to the scheduled headway, very low headway variation and passenger system times result; however, passengers on board the bus are penalized by extra delay on the bus while waiting at the control point. The dynamic strategy reduces the penalty to passengers delayed on-board the bus at a control point at the expense of a slight increase in overall passenger system time.The results indicate that in most cases, the tradeoff of the slight increase in waiting time for the significant decrease in on-board delay penalty makes the dynamic strategy an acceptable choice.     

A model of bus bunching under reliability-based passenger arrival patterns

If bus service departure times are not completely unknown to the passengers, non-uniform passenger arrival patterns can be expected. We propose that passengers decide their arrival time at stops based on a continuous logit model that considers the risk of missing services. Expected passenger waiting times are derived in a bus system that allows also for overtaking between bus services. We then propose an algorithm to derive the dwell time of subsequent buses serving a stop in order to illustrate when bus bunching might occur. We show that non-uniform arrival patterns can significantly influence the bus bunching process. With case studies we find that, even without exogenous delay, bunching can arise when the boarding rate is insufficient given the level of overall demand. Further, in case of exogenous delay, non-uniform arrivals can either worsen or improve the bunching conditions, depending on the level of delay. We conclude that therefore such effects should be considered when service control measures are discussed.     

A self-adaptive method to equalize headways: Numerical analysis and comparison

In uncontrolled bus systems, buses tend to bunch due to the stochastic nature of traffic flows and passenger demand at bus stops. Although schedules and priori target methods introduce slack time to delay buses at control points to maintain constant headways between successive buses, too much slack required delay passengers on-board. In addition, these methods focus on regular headways and do not consider the rates of convergence of headways after disturbances. We propose a self-adaptive control scheme to equalize the headways of buses with little slack in a single line automatically. The proposed method only requires the information from the current bus at the control point and both its leading and following buses. This elegant method is shown to regulate headways faster than existing methods. In addition, compared to previous self-equalizing methods, the proposed method can improve the travel time of buses by about 12%, while keeping the waiting time of passengers almost the same.     

Bus control strategies in corridors with signalized intersections

This paper proposes a new dynamic bus control strategy aimed at reducing the negative effects of time-headway variations on route performance, based on real-time bus tracking data at stops. In routes with high demand, any delay of a single vehicle ends up causing an unstable motion of buses and producing the bus bunching phenomena. This strategy controls the cruising speed of buses and considers the extension of the green phase of traffic lights at intersections, when a bus is significantly delayed. The performance of this strategy will be compared to the current static operation technique based on the provision of slack times at holding points. An operational model is presented in order to estimate the effects of each controlling strategy, taking into account the vehicle capacity constraint. Control strategies are assessed in terms of passenger total travel time, operating cost as well as on the coefficient of headway variation. The effects of controlling strategies are tested in an idealized bus route under different operational settings and in the bus route of highest demand in Barcelona by simulation. The results show that the proposed dynamic controlling strategy reduces total system cost (user and agency) by 15–40% as well as the coefficient of headway variation 53–78% regarding the uncontrolled case, providing a bus performance similar to the expected when time disturbance is not presented.     

Analytical model for calibrating delay at congested bus stops

Abstract

A model is proposed to calculate the overall operating and delay times spent at bus stops due to passenger boarding and alighting and the time lost to queuing caused by bus stop saturation. A formula for line demand at each stop and the interaction between the buses themselves is proposed and applied to different bus stops depending on the number of available berths. The application of this model has quantified significant operational delays suffered by users and operator due to consecutive bus arrival at stops, even with flows below bus stop capacity.     

Estimation of bus dwell time using univariate time series models

A significant proportion of bus travel time is contributed by dwell time for passenger boarding and alighting. More accurate estimation of bus dwell time (BDT) can enhance efficiency and reliability of public transportation system. Regression and probabilistic models are commonly used in literatures where a set of independent variables are used to define the statistical relationship between BDT and its contributing factors. However, due to technical and monetary constraints, it is not always feasible to collect all the data required for the models to work. More importantly, the contributing factors may vary from one bus route to another. Time series based methods can be of great interest as they require only historical time series data, which can be collected using a facility known as automatic vehicle location (AVL) system. This paper assesses four different time series based methods namely random walk, exponential smoothing, moving average (MA), and autoregressive integrated moving average to model and estimate BDT based on AVL data collected from Auckland. The performances of the proposed methods are ranked based on three important factors namely prediction accuracy, simplicity, and robustness. The models showed promising results and performed differently for central business district (CBD) and non‐CBD bus stops. For CBD bus stops, MA model performed the best, whereas for non‐CBD bus stops, ARIMA model performed the best compared with other time series based models. Copyright © 2014 John Wiley & Sons, Ltd.     

Using non-real-time Automatic Vehicle Location data to improve bus services

Although real-time Automatic Vehicle Location (AVL) data is being utilised successfully in the UK, little notice has been given to the benefits of historical (non-real-time) AVL data. This paper illustrates how historical AVL data can be used to identify segments of a bus route which would benefit most from bus priority measures and to improve scheduling by highlighting locations at which the greatest deviation from schedule occurs. A new methodology which uses historical AVL data and on-bus passenger counts to calculate the passenger arrival rate at stops along a bus route has been used to estimate annual patronage and the speed of buses as they move between stops. Estimating the patronage at stops using AVL data is more cost-effective than conventional methods (such as surveys at stops which require much more manpower) but retains the benefits of accuracy and stop-specific estimates of annual patronage. The passenger arrival rate can then be used to calculate how long buses spend at stops. If the time buses spend at stops is removed from the total time it takes the bus to traverse a link, the remaining amount of time can be assumed to be the time the bus spends moving and hence the moving speed of the bus can be obtained. It was found that estimation of patronage and the speed of buses as they move between stops using AVL data produced results which were comparable with those obtained by other methods. However the main point to note is that this new method of estimating patronage has the potential to provide a larger and superior data set than is otherwise available, at very low cost.     

Maximizing bus discharge flows from multi-berth stops by regulating exit maneuvers

Upon having loaded and unloaded their passengers, buses are often free to exit a multi-berth bus stop without delay. A bus need not wait to perform this exit maneuver, even if it requires circumventing one or more other buses that are still dwelling in the stop’s downstream berths. Yet, many jurisdictions impose restrictions on bus entry maneuvers into a stop to limit disruptions to cars and other buses. Buses are typically prohibited from entering a stop whenever this would require maneuvering around other buses still dwelling in upstream berths. An entering bus is instead required to wait in queue until the upstream berths are vacated.Analytical models are formulated to predict how bus discharge flows from busy, multi-berth stops are affected by allowing buses to freely exit, but not freely enter berths. These models apply when: a bus queue is always present at the stop’s entrance; buses depart the entry queue and enter the stop as per the restriction described above; and the stop is isolated from the effects of nearby traffic signals and other bus stops. We find that for these restricted-entry stops, bus-carrying capacities can often be improved by regulating the exit maneuvers as well. This turns out to be particularly true when the stop’s number of berths is large. Simulations show that these findings still hold when a stop is only moderately busy with entry queues that persist for much, but not all of the time. The simulations also indicate that removing any restrictions on bus exit maneuvers is almost always productive when stops are not busy, such that short entry queues form only on occasion, and only for short periods. We argue why certain simple policies for regulating exit maneuvers would likely enhance bus-stop discharge flows.     

Increasing the accuracy of trip rate information from passive multi-day GPS travel datasets: Automatic trip end identification issues

With the availability of Global Positioning System (GPS) receivers to capture vehicle location, it is now feasible to easily collect multiple days of travel data automatically. However, GPS-collected data are not ready for direct use in trip rate or route choice research until trip ends are identified within large GPS data streams. One common parameter used to divide trips is dwell time, the time a vehicle is stationary. Identifying trips is particularly challenging when there is trip chaining with brief stops, such as picking up and dropping off passengers. It is hard to distinguish these stops from those caused by traffic controls or congestion. Although the dwell time method is effective in many cases, it is not foolproof and recent research indicates use of additional logic improves trip dividing. While some studies incorporating more than dwell time to identify trip ends having been conducted, research including actual trip ends to evaluate the success of trip dividing methods used have been limited. In this research, 12 ten-day real-world GPS travel datasets were used to develop, calibrate and compare three methods to identify trip start points in the data stream. The true start and end points of each trip were identified in advance in the GPS data stream using a supplemental trip log completed by the participants so that the accuracy of each automated trip division method could be measured and compared. A heuristic model, which combines heading change, dwell time and distance between the GPS points and the road network, performs best, correctly identifying 94% of trip ends.     

Quantifying the effects of driver non-compliance and communication system failure in the performance of real-time bus control strategies

Control strategies that prevent bus bunching allow for improvement to the level of service offered by a transit corridor as well as reducing travel time and its variability, thus providing higher reliability to the user. Several optimization models based on the use of real-time information have been shown to achieve this, through the planning of holding of the buses at bus stops. In the majority of the cases the benefits of these models have been estimated assuming ideal operational conditions while only few of them have been tested in real conditions. However, neither the simulation experiment, nor the real implementations have quantified the effects of real-life phenomena that harm the performance of the system, preventing it from achieving the full potential of these control schemes.This paper examines three phenomena that may occur during the operation of a bus service, which would limit the effectiveness of a holding-based control strategy in the sense that some of the planned holdings might not be executed. These phenomena are drivers non-compliance, failure of communication systems with buses, and the combination of both. The objective is to estimate the negative impact these phenomena can have on the benefits of the strategy, and to identify possible measures that could help operators and decision makers to reduce this impact. Both objectives are achieved using the real-time holding model developed by Delgado et al. (2012), which is tested in a simulation environment.     

Identifying trip ends from raw GPS data with a hybrid spatio-temporal clustering algorithm and random forest model: a case study in Shanghai

ABSTRACT

Smartphones have been advocated as the preferred devices for travel behavior studies over conventional surveys. But the primary challenges are candidate stops extraction from GPS data and trip ends distinction from noise. This paper develops a Resident Travel Survey System (RTSS) for GPS data collection and travel diary verification, and then uses a two-step method to identify trip ends. In the first step, a density-based spatio-temporal clustering algorithm is proposed to extract candidate stops from trajectories. In the second step, a random forest model is applied to distinguish trip ends from mode transfer points. Results show that the clustering algorithm achieves a precision of 96.2%, a recall of 99.6%, mean absolute error of time within 3?min, and average offset distance within 30 meters. The comprehensive accuracy of trip ends identification is 99.2%. The two-step method performs well in trip ends identification and promotes the efficiency of travel survey systems.     

Modeling schedule recovery processes in transit operations for bus arrival time prediction

Many existing algorithms for bus arrival time prediction assume that buses travel at free‐flow speed in the absence of congestion. As a result, delay incurred at one stop would propagate to downstream stops at the same magnitude. In reality, skilled bus operators often constantly adjust their speeds to keep their bus on schedule. This paper formulates a Markov chain model for bus arrival time prediction that explicitly captures the behavior of bus operators in actively pursuing schedule recovery. The model exhibits some desirable properties in capturing the schedule recovery process. It guarantees provision of the schedule information if the probability of recovering from the current schedule deviation is sufficiently high. The proposed model can be embedded into a transit arrival time estimation model for transit information systems that use both real‐time and schedule information. It also has the potential to be used as a decision support tool to determine when dynamic or static information should be used.     

Towards modal choice decision-making models: A case study of Lagos state transport corporation bus riders

Identification of the socioeconomic factors which affect the demand for buses, and the analysis of the use of the other transport modes by bus users are the two main objectives of this article. Work and school trips are highlighted as being very important trip purposes in Lagos metropolis by the multiple discriminant analysis model. It identifies mode of transport, distance, travel time, reliability, and the number of stops as significant mode choice variables. Multiple linear regression models for work and school trips identify mode of transport, transfort fare, travel time, annual income, and crew behaviour as significant variables in the choice of transport mode. These findings support the two alternative hypotheses of the study that the choice of bus is related to the individual perception of the quality of service of the different modes and that socioeconomic characteristics of the riders influence the patronage of buses. The attention of policy makers for the 22 transport corporations that operate inter-and intra-urban services in all the 21 states and the federal capital of Abuja in Nigeria is drawn to the importance of these variables for decisions.     

A congested and dwell time dependent transit corridor assignment model

This research proposes an equilibrium assignment model for congested public transport corridors in urban areas. In this model, journey times incorporate the effect of bus queuing on travel times and boarding and alighting passengers on dwell times at stops. The model also considers limited bus capacity leading to longer waiting times and more uncomfortable journeys. The proposed model is applied to an example network, and the results are compared with those obtained in a recent study. This is followed by the analysis and discussion of a real case application in Santiago de Chile. Finally, different boarding and alighting times and different vehicle types are evaluated. In all cases, demand on express services tends to be underestimated by using constant dwell time assignment models, leading to potential planning errors for these lines. The results demonstrate the importance of considering demand dependent dwell times in the assignment process, especially at high demand levels when the capacity constraint should also be considered. Copyright © 2017 John Wiley & Sons, Ltd.