A connected-vehicle-based dynamic control model for managing the bus bunching problem with capacity constraints

This paper describes a connected-vehicle-based system architecture which can provide more precise and comprehensive information on bus movements and passenger status. Then a dynamic control method is proposed using connected vehicle data. Traditionally, the bus bunching problem has been formulated into one of two types of optimization problem. The first uses total passenger time cost as the objective function and capacity, safe headway, and other factors as constraints. Due to the large number of scenarios considered, this type of framework is inefficient for real-time implementation. The other type uses headway adherence as the objective and applies a feedback control framework to minimize headway variations. Due to the simplicity in the formulation and solution algorithms, the headway-based models are more suitable for real-time transit operations. However, the headway-based feedback control framework proposed in the literature still assumes homogeneous conditions at all bus stations, and does not consider restricting passenger loads within the capacity constraints. In this paper, a dynamic control framework is proposed to improve not only headway adherence but also maintain the stability of passenger load within bus capacity in both homogenous and heterogeneous situations at bus stations. The study provides the stability conditions for optimal control with heterogeneous bus conditions and derives optimal control strategies to minimize passenger transit cost while maintaining vehicle loading within capacity constraints. The proposed model is validated with a numerical analysis and case study based on field data collected in Chengdu, China. The results show that the proposed model performs well on high-demand bus routes.     

A generic data assimilation framework for vehicle trajectory reconstruction on signalized urban arterials using particle filters

With trajectory data, a complete microscopic and macroscopic picture of traffic flow operations can be obtained. However, trajectory data are difficult to observe over large spatiotemporal regions—particularly in urban contexts—due to practical, technical and financial constraints. The next best thing is to estimate plausible trajectories from whatever data are available. This paper presents a generic data assimilation framework to reconstruct such plausible trajectories on signalized urban arterials using microscopic traffic flow models and data from loops (individual vehicle passages and thus vehicle counts); traffic control data; and (sparse) travel time measurements from whatever source available. The key problem we address is that loops suffer from miss- and over-counts, which result in unbounded errors in vehicle accumulations, rendering trajectory reconstruction highly problematic. Our framework solves this problem in two ways. First, we correct the systematic error in vehicle accumulation by fusing the counts with sparsely available travel times. Second, the proposed framework uses particle filtering and an innovative hierarchical resampling scheme, which effectively integrates over the remaining error distribution, resulting in plausible trajectories. The proposed data assimilation framework is tested and validated using simulated data. Experiments and an extensive sensitivity analysis show that the proposed method is robust to errors both in the model and in the measurements, and provides good estimations for vehicle accumulation and vehicle trajectories with moderate sensor quality. The framework does not impose restrictions on the type of microscopic models used and can be naturally extended to include and estimate additional trajectory attributes such as destination and path, given data are available for assimilation.     

Mesoscopic simulation for transit operations

This paper presents a transit simulation model designed to support evaluation of operations, planning and control, especially in the context of Advanced Public Transportation Systems (APTS). Examples of potential applications include frequency determination, evaluation of real-time control strategies for schedule maintenance and assessing the effects of vehicle scheduling on the level of service. Unlike most previous efforts in this area, the simulation model is built on a platform of a mesoscopic traffic simulation model, which allows modeling of the operation dynamics of large-scale transit systems taking into account the stochasticity due to interactions with road traffic. The capabilities of Mezzo as an evaluation tool of transit operations are demonstrated with an application to a real-world high-demand bus line in the Tel-Aviv metropolitan area under various scenarios. The headway distributions at two stops are compared with field observations and show good consistency between simulated and observed data.     

A simulation model of the bus transitway (TRNSIM)

For planning and design of a bus rapid transit system and for the analysis of multimodal corridors, methodology is required for simulating bus traffic operation on a Transitway. Macroscopic models of vehicle flow are gaining popularity due to their capability to analyze complex operations and yet offer efficiency in development and applications. A macroscopic model is developed for the investigation of travel time, energy and emissions that correspond to bus volume levels on the Transitway. This paper describes the travel time part of the model. The model treats stochastic characteristics of bus traffic and passenger activities. Also, safety regimes in vehicle flow and factors affecting minimum headways in station areas are incorporated. The model is verified by comparing simulated travel time for the Ottawa-Carleton Transitway with actual data.     

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.     

Online calibration of an integrated framework for information‐based evacuation operations

This study seeks to online calibrate the parameters of aggregate evacuee behavior models used in a behavior‐consistent information‐based control module for determining information strategies for real‐time evacuation operations. It enables the deployment of an operational framework for mass evacuation that integrates three aspects underlying an evacuation operation: demand (evacuee behavior), supply (network management), and disaster characteristics. To attain behavior‐consistency, the control module factors evacuees' likely responses to the disseminated information in determining information‐based control strategies. Hence, the ability of the behavior models to predict evacuees' likely responses is critical to the effectiveness of traffic routing by information strategies. The mixed logit structure is used for the aggregate behavior models to accommodate the behavioral heterogeneity across the population. An online calibration problem is proposed to calibrate the random parameters in the behavior models by using the least square estimator to minimize the gap between the predicted network flows and unfolding traffic dynamics. Background traffic, an important but rarely studied issue for modeling evacuation traffic, is also accounted for in the proposed problem. Numerical experiments are conducted to illustrate the importance of the calibration problem for addressing the system consistency issues and integrating the demand, supply, and disaster characteristics for more efficient evacuation operations. Copyright © 2016 John Wiley & Sons, Ltd.     

Investigations of interactions between bus rapid transit and general traffic flows

Bus rapid transit (BRT) is a popular strategy to increase transit attraction because of its high‐capacity, comfortable service, and fast travel speed with the exclusive right‐of‐way. Various engineering designs of right‐of‐way and the violation enforcement influence interactions between BRT and general traffic flows. An empirical assessment framework is proposed to investigate traffic congestion and lane‐changing patterns at one typical bottleneck along a BRT corridor. The BRT bottleneck consists of bus lane, BRT station, video enforcement zone, and transit signal priority intersection. We analyze oblique cumulative vehicle counts and oblique cumulative lane‐changing maneuvers extracted from videos. The cumulative vehicle counts method widely applied in revealing queueing dynamics at freeway bottlenecks is extended to an urban BRT corridor. In the study site, we assume four lane‐changing patterns, three of which are verified by the empirical measurements. Investigations of interactions between buses and general traffic show that abnormal behaviors (such as lane violations and slow moving of the general traffic) induce 16% reduction in the saturation rate of general traffic and 17% increase in bus travel time. Further observations show that the BRT station and its induced increasing lane‐changing maneuvers increase the downstream queue discharge flows of general traffic. The empirical results also contribute to more efficient strategies of BRT planning and operations, such as alternative enforcement methods, various lane separation types, and optimized traffic operations. Copyright © 2014 John Wiley & Sons, Ltd.     

A hybrid scheme for real‐time prediction of bus trajectories

The uncertainty associated with public transport services can be partially counteracted by developing real‐time models to predict downstream service conditions. In this study, a hybrid approach for predicting bus trajectories by integrating multiple predictors is proposed. The prediction model combines schedule, instantaneous and historical data. The contribution of each predictor as well as values of respective parameters is estimated by minimizing the prediction error using a linear regression heuristic. The hybrid method was applied to five bus routes in Stockholm, Sweden, and Brisbane, Australia. The results indicate that the hybrid method consistently outperforms the timetable and delay conservation prediction method for different route layouts, passenger demands and operation practices. Model validation confirms model transferability and real‐time applicability. Generating more accurate predictions can help service users adjust their travel plans and service providers to deploy proactive management and control strategies to mitigate the negative effects of service disturbances. Copyright © 2017 John Wiley & Sons, Ltd.     

Closing the loop in real-time railway control: Framework design and impacts on operations

Railway traffic is heavily affected by disturbances and/or disruptions, which are often cause of delays and low performance of train services. The impact and the propagation of such delays can be mitigated by relying on automatic tools for rescheduling traffic in real-time. These tools predict future track conflict based on current train information and provide suitable control measures (e.g. reordering, retiming and/or rerouting) by using advanced mathematical models. A growing literature is available on these tools, but their effects on real operations are blurry and not yet well known, due to the very scarce implementation of such systems in practice.In this paper we widen the knowledge on how automatic real-time rescheduling tools can influence train performance when interfaced with railway operations. To this purpose we build up a novel traffic control framework that couples the state-of-the art automatic rescheduling tool ROMA, with the realistic railway traffic simulation environment EGTRAIN, used as a surrogate of the real field. At regular times ROMA is fed with current traffic information measured from the field (i.e. EGTRAIN) in order to predict possible conflicts and compute (sub) optimal control measures that minimize the max consecutive delay on the network. We test the impact of the traffic control framework based on different types of interaction (i.e. open loop, multiple open loop, closed loop) between the rescheduling tool and the simulation environment as well as different combinations of parameter values (such as the rescheduling interval and prediction horizon). The influence of different traffic prediction models (assuming e.g. aggressive versus conservative driving behaviour) is also investigated together with the effects on traffic due to control delays of the dispatcher in implementing the control measures computed by the rescheduling tool.Results obtained for the Dutch railway corridor Utrecht–Den Bosch show that a closed loop interaction outperforms both the multiple open loop and the open loop approaches, especially with large control delays and limited information on train entrance delays and dwell times. A slow rescheduling frequency and a large prediction horizon improve the quality of the control measure. A limited control delay and a conservative prediction of train speed help filtering out uncertain traffic dynamics thereby increasing the effectiveness of the implemented measures.     

Microscopic simulation of transit operations: policy studies with the MISTRANSIT application programming interface

Abstract

Microscopic traffic simulators are the most advanced tools for representing the movement of vehicles on a transport network. However, the energy spent in traffic microsimulation has been mainly oriented to cars. Little interest has been devoted to more sophisticated models for simulating transit systems. Commercial software has some options to incorporate the operation of transit vehicles, but they are insufficient to properly consider a real public transport system. This paper develops an Application Programming Interface, called MIcroscopic Simulation of TRANSIT (MISTRANSIT), using the commercial microsimulator PARAllel MICroscopic Simulation. MISTRANSIT makes advances in three ways: public transport vehicles can have new characteristics; passengers are incorporated and traced as individual objects; and specific models represent the interaction between passengers and vehicles at stops. This paper presents the modelling approach as well as various experiments to illustrate the feasibility of MISTRANSIT for studying policy operations of transit systems.     

Priority for public transit in Germany

Priority for public transit includes a large variety of measures, including improvements to infrastructure and vehicles. For vehicles, the low floor concept is of particular importance. The central points of priority measures, however, are improvements of traffic control by traffic signals. Here, an improved sensitivity regarding public transit vehicles is the key to a remarkable reduction of factors causing delay. Different techniques for a traffic actuated signal control and different strategies regarding the degree of priority are applied. Thus, especially the reliability of public transit operations is increased. The priority efforts must be embedded in an integrated plan covering the whole urban or metropolitan transportation system.     

A Modeling Framework for Bus Rapid Transit Operations Evaluation and Service Planning

Abstract

In this paper, we present a dynamic traffic assignment-simulation modeling framework (DYNASMART-P) to support the evaluation and planning of Bus Rapid Transit (BRT) services in urban transportation networks. The model represents the different characteristics associated with BRT operations such as: exclusive right-of-way lanes, limited-stop service, signal prioritization at congested intersections, and enhanced bus stops to reduce passenger boarding times. A set of simulation experiments is conducted using the model to study the impact of introducing a hypothetical BRT service in the Knoxville area in the State of Tennessee. In these experiments, the different operational characteristics of BRT are evaluated in terms of potential impact on transit ridership and on the interacting auto traffic. The results illustrate the advantages of BRT for increasing transit ridership and improving overall system performance.     

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.     

Microsimulation models incorporating both demand and supply dynamics

There has been rapid growth in interest in real-time transport strategies over the last decade, ranging from automated highway systems and responsive traffic signal control to incident management and driver information systems. The complexity of these strategies, in terms of the spatial and temporal interactions within the transport system, has led to a parallel growth in the application of traffic microsimulation models for the evaluation and design of such measures, as a remedy to the limitations faced by conventional static, macroscopic approaches. However, while this naturally addresses the immediate impacts of the measure, a difficulty that remains is the question of how the secondary impacts, specifically the effect on route and departure time choice of subsequent trips, may be handled in a consistent manner within a microsimulation framework.The paper describes a modelling approach to road network traffic, in which the emphasis is on the integrated microsimulation of individual trip-makers’ decisions and individual vehicle movements across the network. To achieve this it represents directly individual drivers’ choices and experiences as they evolve from day-to-day, combined with a detailed within-day traffic simulation model of the space–time trajectories of individual vehicles according to car-following and lane-changing rules and intersection regulations. It therefore models both day-to-day and within-day variability in both demand and supply conditions, and so, we believe, is particularly suited for the realistic modelling of real-time strategies such as those listed above. The full model specification is given, along with details of its algorithmic implementation. A number of representative numerical applications are presented, including: sensitivity studies of the impact of day-to-day variability; an application to the evaluation of alternative signal control policies; and the evaluation of the introduction of bus-only lanes in a sub-network of Leeds. Our experience demonstrates that this modelling framework is computationally feasible as a method for providing a fully internally consistent, microscopic, dynamic assignment, incorporating both within- and between-day demand and supply dynamics.     

Trajectory data reconstruction and simulation-based validation against macroscopic traffic patterns

This paper shows that the behavior of driver models, either individually or entangled in stochastic traffic simulation, is affected by the accuracy of empirical vehicle trajectories. To this aim, a “traffic-informed” methodology is proposed to restore physical and platoon integrity of trajectories in a finite time–space domain, and it is applied to one NGSIM I80 dataset. However, as the actual trajectories are unknown, it is not possible to verify directly whether the reconstructed trajectories are really “nearer” to the actual unknowns than the original measurements. Therefore, a simulation-based validation framework is proposed, that is also able to verify indirectly the efficacy of the reconstruction methodology. The framework exploits the main feature of NGSIM-like data that is the concurrent view of individual driving behaviors and emerging macroscopic traffic patterns. It allows showing that, at the scale of individual models, the accuracy of trajectories affects the distribution and the correlation structure of lane-changing model parameters (i.e. drivers heterogeneity), while it has very little impact on car-following calibration. At the scale of traffic simulation, when models interact in trace-driven simulation of the I80 scenario (multi-lane heterogeneous traffic), their ability to reproduce the observed macroscopic congested patterns is sensibly higher when model parameters from reconstructed trajectories are applied. These results are mainly due to lane changing, and are also the sought indirect validation of the proposed data reconstruction methodology.     

Investigating the irregularity of bus routes: highlighting how underlying assumptions of bus models impact the regularity results

A bus route is inherently unstable: when the system is uncontrolled, buses fail to maintain their time‐headways and tend to bunch. Several mathematical bus motion models were proposed to reproduce the bus behavior and assess management strategies. However, no work has established how the choice of a model impacts the irregularity of modeled bus systems, that is, the non‐respect of scheduled headways. Because of this gap, a large body of existing works assumes that the ability of these models to reproduce instability comes only from stochasticity, although the link between stochastic inputs and the level of irregularity remains unknown. Moreover, some recognized phenomena such as a change of travel conditions during a day or delays at signalized intersections are ignored. To address these shortcomings, this paper provides an overview of existing dynamic bus‐focused models and proposes a simple way to classify them. Commonly used deterministic and stochastic models are compared, which allows quantifying the relative influence of stochasticity of each model component on outputs. Moreover, we show that a change in the system equilibrium in a full deterministic system can lead to irregularity. Finally, this paper proposes a refinement of travel time models to account for non‐dynamic signals. In presence of traffic signals, we show that a bus system can be self‐regulated. Especially, these insights could help to calibrate bus model inputs to better reproduce real data. Copyright © 2014 John Wiley & Sons, Ltd.     

Transit stop inspection and maintenance scheduling: A GPU accelerated metaheuristics approach

Bus stops are integral elements of a transit system and as such, their efficient inspection and maintenance is required, for proper and attractive transit operations. Nevertheless, spatial dispersion and the extensive number of bus stops, even for mid-size transit systems, complicates scheduling of inspection and maintenance tasks. In this context, the problem of scheduling transit stop inspection and maintenance activities (TSIMP) by a two-stage optimization approach, is formulated and discussed. In particular, the first stage involves districting of the bus stop locations into areas of responsibility for different inspection and maintenance crews (IMCs), while in the second stage, determination of the sequence of bus stops to be visited by an IMC is modelled as a vehicle routing problem. Given the complexity of proposed optimization models, advanced versions of different metaheuristic algorithms (Harmony Search and Ant Colony Optimization) are exploited and assessed as possible options for solving these models. Furthermore, two variants of ACO are implemented herein; one implemented into a CPU parallel computing environment along with an accelerated one by means of general-purpose graphics processing unit (GPGPU) computing. The model and algorithms are applied to the Athens (Greece) bus system, whose extensive number of transit stops (over 7500) offers a real-world test bed for assessing the potential of the proposed modelling approach and solution algorithms. As it was shown for the test example examined, both algorithms managed to achieve optimized solutions for the problem at hand while there were fund robust with respect to their algorithmic parameters. Furthermore, the use of graphics processing units (GPU) managed to reduce of computational time required.     

How to assess the benefits of connected vehicles? A simulation framework for the design of cooperative traffic management strategies

Advances in Information and Communication Technologies (ICT) allow the transportation community to foresee dramatic improvements for the incoming years in terms of a more efficient, environmental friendly and safe traffic management. In that context, new ITS paradigms like Cooperative Systems (C-ITS) enable an efficient traffic state estimation and traffic control. C-ITS refers to three levels of cooperation between vehicles and infrastructure: (i) equipped vehicles with Advanced Driver Assistance Systems (ADAS) adjusting their motion to surrounding traffic conditions; (ii) information exchange with the infrastructure; (iii) vehicle-to-vehicle communication. Therefore, C-ITS makes it possible to go a step further in providing real time information and tailored control strategies to specific drivers. As a response to an expected increasing penetration rate of these systems, traffic managers and researchers have to come up with new methodologies that override the classic methods of traffic modeling and control. In this paper, we discuss some potentialities of C-ITS for traffic management with the methodological issues following the expansion of such systems. Cooperative traffic models are introduced into an open-source traffic simulator. The resulting simulation framework is robust and able to assess potential benefits of cooperative traffic control strategies in different traffic configurations.     

Survey and empirical evaluation of nonhomogeneous arrival process models with taxi data

Arrival processes are important inputs to many transportation system functions, such as vehicle prepositioning, taxi dispatch, bus holding strategies, and dynamic pricing. We conduct a comprehensive survey of the literature which shows that many transport systems employ basic homogeneous arrival process models or static nonhomogeneous processes. We conduct an empirical experiment to compare five state of the art arrival process short term prediction models using a common transportation system data set: New York taxi passenger pickups in 2013. Pickup data is split between 672 observations for model estimation and 96 observations for validation. From our experiment, we obtain evidence to support a recent model called FM‐IntGARCH, which is able to combine the benefits of both time series models and discrete count processes. Using a set of seven performance metrics from the literature, FM‐IntGARCH is shown to outperform the offline models—seasonal factor method, piecewise linear model—as well as the online models—ARIMA, Gaussian Cox process. Implications for operating data‐driven “smart” transit systems and urban informatics are discussed. Copyright © 2016 John Wiley & Sons, Ltd.