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Highlights • Algorithm for high-dimensional dynamic OD calibration for large-scale networks. • High-dimensional problem with 16,200 decision variables for a large-scale Singapore network. • Proposed method improves the objective function by 1 order of magnitude compared to benchmark methods. • Improves fit to link counts by 77% compared to benchmark methods. Abstract This paper considers offline dynamic OD (origin-destination) calibration problems for large-scale simulation-based network models. We formulate the problem as a simulation-based optimization (SO) problem and propose a scalable and efficient metamodel SO algorithm. For a network with n links and a problem with T time intervals and Z OD pairs per time interval, the corresponding SO problem is a high-dimensional problem of dimension T · Z. At each iteration of the SO algorithm, we solve a set of T independent analytical and differentiable metamodel optimization problems, each of dimension Z. The T analytical problems are constrained by n nonlinear equality constraints. Hence, they scale linearly with the number of links in the network and independently of other network attributes, such as the dimension of the route choice set or the link lengths. The temporal correlation, of the link performance metrics across time intervals, is approximately captured through the parameters of the metamodel. Since the T analytical optimization problems are decoupled and can be solved independently, the proposed approach scales independently of the number of calibration time intervals, making it suitable for the calibration of demand over numerous time periods. The approach is efficient and scalable. It is suitable to address high-dimensional calibration problems for large-scale network models. It is benchmarked on both a toy network and a Singapore network versus two general-purpose algorithms: SPSA and a derivative-free pattern search algorithm. The validation experiments indicate that the proposed method identifies points with objective function estimates that outperform the benchmark methods by 1 to 2 orders of magnitude. As the problem dimension and the temporal correlation, across time intervals, increase, so does the magnitude by which the proposed method outperforms the benchmark methods. The Singapore case study considers a problem of dimension 16,200. This is 2 orders of magnitude higher than what is currently considered high-dimensional for continuous SO problems. The proposed method identifies solutions with an estimated objective function that is 1 order of magnitude better than those of the benchmark methods. It yields solutions that provide an average, across time intervals, improvement to link counts of 77%, compared to the benchmark methods. The case study also illustrates the robustness of the method to the quality of the initial points. [ABSTRACT FROM AUTHOR]

Highlights • The vehicle stock imbalance problem in one-way vehicle sharing systems is addressed. • User-based vehicle relocations are used to avoid supply-and-demand mismatches. • Dynamic allocations of vehicles to users are based on incentivization schemes. • Price incentives reward users acting in favor of the system. • Incentives are based on the priorities of vehicle relocations among stations. Abstract The asymmetric demand-offer problem represents a major challenge for one-way vehicle sharing systems (VSS) affecting their economic viability as it necessitates the engagement of considerable human (and financial) resources in relocating vehicles to satisfy customer demand. In this paper, we propose a novel approach which involves user-based vehicle relocations to address supply-and-demand mismatches; in our approach, VSS users are offered price incentives so as to accept picking up their vehicle from an oversupplied station and/or to drop it off to an under-supplied station. The system incentivizes users based on the priorities of vehicle relocations among stations, taking into account the fluctuating demand for vehicles and parking places at different stations over time. A graph-theoretic approach is employed for modeling the problem of allocating vehicles to users in a way that maximizes the profit of the system taking into account the budget the VSS can afford to spend for rewarding users, as well as the users' strategic behavior. We present two different schemes for incentivizing users to act in favour of the system. Both schemes consider budget constraints and are truthful and budget-feasible. We have extensively evaluated our approach through simulations which demonstrated significant gain with respect to the number of completed trips and system revenue. We have also validated our approach through pilot trials conducted in a free-floating e-motorbike sharing system in the framework of an EU-funded research project. [ABSTRACT FROM AUTHOR]

Highlights • Model heterogeneous flows in the interdependent infrastructure system. • Consider partial capacity losses under facility disruption risks. • Formulate a non-linear integer programming model for the studied problem. • Draw insights into the features of the proposed model with numerical examples. • Reveal managerial insights into optimal location design and facility improvement. Abstract Due to the interdependency between multiple infrastructure systems, the performance of a facility may depend on the resources or supplies received from other facilities. However, cross-system interdependence has seldom been studied in the location design context, probably due to the lack of a concise model describing interdependence across heterogeneous systems. This paper proposes a new heterogeneous flow scheme to describe cross-system interdependence. This scheme has two features distinguished from existing models in describing an interdependent facility location problem. First, it is a simple linear model upon which a compact facility location model can be built. Secondly, it relaxes the need to maintain flow conservation between different systems and is suitable in describing heterogeneous systems that take in and output different resources or services. Built on this scheme, this paper proposes a reliable location design model for a nexus of interdependent infrastructure systems. This model aims to locate the optimal facility locations in multiple heterogeneous systems to balance the tradeoff between the facility investment and the expected nexus operation performance. Different from other reliable facility location models, this expected performance captures interdependence among heterogeneous systems due to the resource input-output relationships. The consideration of continuous partial capacity losses complements the reliable location literature that mainly focuses on binary disruptions. Two numerical examples are conducted for investigating features and applications of the proposed model. The results indicate that with a standard off-the-shelf integer programming solver, the proposed model is able to solve optimal facility location design for problem instances of realistic scales to the near-optimum solutions with optimality gap assurance. Sensitivity analyses of key parameters indicate that improving facility capacity and reducing interdependency between systems can mitigate impacts of facility capacity losses and reduce the overall system cost. [ABSTRACT FROM AUTHOR]

Highlights • A data analytics framework is developed to characterize air traffic flows. • Trajectory patterns and traffic flow patterns are learned from flight tracks. • Terminal area operations in three multi-airport systems are compared. • Structural, operational and performance differences are identified. • The most dynamic system shows the highest average traffic flow efficiency. Abstract Future Air Traffic Management systems can benefit from innovative approaches that leverage the increasing availability of operational data to facilitate the development of new performance assessment and decision-support capabilities. This paper presents a data analytics framework for high-fidelity characterization of air traffic flows from large-scale flight tracking data. Machine learning methods are used to exploit spatiotemporal patterns in aircraft movement towards the identification of trajectory patterns and traffic flow patterns. The outcomes and potential impacts of this framework are demonstrated with a comparative analysis of terminal area operations in three representative multi-airport (metroplex) systems of the global air transportation system: New York, Hong Kong and Sao Paulo. As a descriptive tool for systematic analysis of the flow behavior, the framework allows for cross-metroplex comparisons of terminal airspace design, utilization and traffic performance. Novel quantitative metrics are created to summarize metroplex efficiency, capacity and predictability. The results reveal several structural, operational and performance differences between the multi-airport systems analyzed. Our findings show that New York presents the most complex airspace design, with considerably higher number of routes and interactions between them, as well as more dynamic changes in the terminal area flow structure during the day, in part driven by the presence of flow dependencies. Interestingly, it exhibits the best levels of traffic flow efficiency on average, both spatially and temporally, yet the highest variability in metroplex configuration performance, with more pronounced performance degradation during inclement weather. [ABSTRACT FROM AUTHOR]

Highlights • We examine traffic management strategies in a dynamic evacuation network. • A network optimization model featuring a bi-level scheme is proposed. • The proposed model finds the best distribution of cross-elimination and signal control strategies. Abstract It is well accepted in literature that the strategy of using crossing-elimination (or uninterrupted-flow) intersections has the advantage over signals in expanding network capacity during evacuation. This result holds well in a static evacuation network in which flow patterns are not explicitly modeled (e.g., queuing on network links and time-varying evacuation demand). This paper examines the problem of selecting and distributing signal control and uninterrupted flow strategies in evacuation traffic management. More specifically, the following critical issues are investigated: (1) Does an optimal distribution between signal and uninterrupted flow strategies exist in a dynamic evacuation network and what is it? and (2) How to best plan turning restriction and signal timings at those intersections with different control strategies? Results show that the best distribution of uninterrupted flow and signalized intersections exists to yield the minimum evacuation clearance time. Intersections located within the demand generating area (i.e., the impacted area) are more likely to be selected by the model as uninterrupted flow ones to facilitate fast access of evacuees to the evacuation routes; while signals tend to locate outside the impact area to reduce detours of evacuation traffic to destination. The proposed model outperforms other scenarios in terms of reducing evacuation clearance time, which demonstrates its effectiveness. [ABSTRACT FROM AUTHOR]

Highlights • Apply ensemble learning techniques to improve airline fuel burn prediction. • Propose a prediction interval based discretionary fuel estimation approach. • The new approach can achieve $61 million annual fuel savings for the study airline. • CO 2 emission can also be reduced by 428 million kg per year for the study airline. Abstract Reducing fuel consumption is a unifying goal across the aviation industry. One fuel-saving opportunity for airlines is the possibility of reducing discretionary fuel loading by dispatchers. In this study, we propose a novel discretionary fuel estimation approach that can assist dispatchers with better discretionary fuel loading decisions. Based on the analysis on our study airline, our approach is found to substantially reduce unnecessary discretionary fuel loading while maintaining the same safety level compared to the current fuel loading practice. The idea is that by providing dispatchers with more accurate information and better recommendations derived from flight records, unnecessary fuel loading and corresponding cost-to-carry could both be reduced. We apply ensemble learning techniques to improve fuel burn prediction and construct prediction intervals (PIs) to capture the uncertainty of model predictions. The upper bound of a PI can then be used for discretionary fuel loading. The potential benefit of this approach is estimated to be $61.5 million in fuel savings and 428 million kg of CO 2 reduction per year for our study airline. This study also builds a link between discretionary fuel estimation and aviation system predictability in which the proposed models can also be used to predict benefits from reduced fuel loading enabled by improved Air Traffic Management (ATM) targeting on improved system predictability. [ABSTRACT FROM AUTHOR]

Highlights • Estimating historical traffic volumes between sparsely-located traffic sensors is addressed. • Neural network yields 24% more accurate results than the state of the practice approach. • Value of vehicle probe data in estimating historical traffic volumes is quantified. Abstract This paper focuses on the problem of estimating historical traffic volumes between sparsely-located traffic sensors, which transportation agencies need to accurately compute statewide performance measures. To this end, the paper examines applications of vehicle probe data, automatic traffic recorder counts, and neural network models to estimate hourly volumes in the Maryland highway network, and proposes a novel approach that combines neural networks with an existing profiling method. On average, the proposed approach yields 24% more accurate estimates than volume profiles, which are currently used by transportation agencies across the US to compute statewide performance measures. The paper also quantifies the value of using vehicle probe data in estimating hourly traffic volumes, which provides important managerial insights to transportation agencies interested in acquiring this type of data. For example, results show that volumes can be estimated with a mean absolute percent error of about 21% at locations where average number of observed probes is between 30 and 47 vehicles/h, which provides a useful guideline for assessing the value of probe vehicle data from different vendors. [ABSTRACT FROM AUTHOR]

Highlights • Coordinating actions of neighbouring local MPC controllers approximates green waves. • The number of scenarios to be considered for optimal selection of switching times can be reduced significantly by using existing traffic control strategies. • Exchange of messages about planned switching scenarios among neighbouring intersections improves performance thanks to better local predictions. • Combining delay minimizing coordinated MPC with max pressure control reduces the growth of distant downstream queues. Abstract The present paper describes how to use coordination between neighbouring intersections in order to improve the performance of urban traffic controllers. Both the local MPC (LMPC) introduced in the companion paper (Hao et al., 2018) and the coordinated MPC (CMPC) introduced in this paper use the urban cell transmission model (UCTM) (Hao et al., 2018) in order to predict the average delay of vehicles in the upstream links of each intersection, for different scenarios of switching times of the traffic lights at that intersection. The feedback controller selects the next switching times of the traffic light corresponding to the shortest predicted average delay. While the local MPC (Hao et al., 2018) only uses local measurements of traffic in the links connected to the intersection in comparing the performance of different scenarios, the CMPC approach improves the accuracy of the performance predictions by allowing a control agent to exchange information about planned switching times with control agents at all neighbouring intersections. Compared to local MPC the offline information on average flow rates from neighbouring intersections is replaced in coordinated MPC by additional online information on when the neighbouring intersections plan to send vehicles to the intersection under control. To achieve good coordination planned switching times should not change too often, hence a cost for changing planned schedules from one decision time to the next decision time is added to the cost function. In order to improve the stability properties of CMPC a prediction of the sum of squared queue sizes is used whenever some downstream queues of an intersection become too long. Only scenarios that decrease this sum of squares of local queues are considered for possible implementation. This stabilization criterion is shown experimentally to further improve the performance of our controller. In particular it leads to a significant reduction of the queues that build up at the edges of the traffic region under control. We compare via simulation the average delay of vehicles travelling on a simple 4 by 4 Manhattan grid, for traffic lights with pre-timed control, traffic lights using the local MPC controller (Hao et al., 2018), and coordinated MPC (with and without the stabilizing condition). These simulations show that the proposed CMPC achieves a significant reduction in delay for different traffic conditions in comparison to these other strategies. [ABSTRACT FROM AUTHOR]

Highlights • Apply CTM to urban traffic including traffic lights, queue discharge, merge/diverge. • Implement and validate proposed model by comparing to SUMO results, and explain differences. • Develop local MPC selecting switching times of traffic lights, at time resolution of a few seconds. • Evaluate performance of traffic system using local MPC control for traffic lights, for small urban region. Abstract This paper is the first in a series of reports presenting a framework for the hierarchical design of feedback controllers for traffic lights in urban networks. The goal of the research is to develop an easy to understand methodology for designing model based feedback controllers that use the current state estimate in order to select the next switching times of traffic lights. In this paper we introduce an extension of the cell transmission model that describes with sufficient accuracy the major causes of delay for urban traffic. We show that this model is computationally fast enough such that it can be used in a model predictive controller that decides for each intersection, taking into account the vehicle density as estimated along all links connected to the intersection, what switching time minimizes the local delay for all vehicles over a prediction horizon of a few minutes. The implementation of this local MPC only requires local online measurements and local model information (unlike the coordinated MPC, to be introduced in the next paper in this series, that takes into account interactions between neighbouring intersections). We study the performance of the proposed local MPC via simulation on a simple 4 by 4 Manhattan grid, comparing its delay with an efficiently tuned pretimed control for the traffic lights, and with traffic lights controlled according to the max pressure rule. These simulations show that the proposed local MPC controller achieves a significant reduction in delay for various traffic conditions. [ABSTRACT FROM AUTHOR]

Abstract In this paper we propose a new aggregated Variable-Length cell transmission Model (VLM) for traffic systems consisting of only three lumped state variables. The first two state variables are the lumped (or averaged) densities associated with the two cells of variable length into which the considered road section is subdivided. These cells are, respectively, a downstream congested cell, of length l , and an upstream free cell, of length L - l , L being the total length of the section. The third state, namely l , describes the position of the congestion wave front. The paper, apart from providing the new traffic model formulation, also investigates several mathematical properties of this model, ensuring the consistency with respect to the inherent mass (or vehicle) conservation law. Two case studies, a ring road and a urban road with traffic lights, are considered to assess the proposed model validity also relying on simulation evidence. [ABSTRACT FROM AUTHOR]

Highlights • A theoretical framework is developed for estimating multi-year 24/7 O-D demand. • The data-driven framework utilizes high-granular traffic counts and speed data collected over many years. • Multi-year traffic data are statistically clustered into typical traffic patterns. • A GPU-based stochastic projected gradient descent method is proposed to efficiently solve large-scale DODE problem. • Multi-year 24/7 dynamic OD demand reveal daily, weekly, monthly, seasonal and yearly change in travel time. Abstract Dynamic origin-destination (OD) demand is central to transportation system modeling and analysis. The dynamic OD demand estimation problem (DODE) has been studied for decades, most of which solve the DODE problem on a typical day or several typical hours. There is a lack of methods that estimate high-resolution dynamic OD demand for a sequence of many consecutive days over several years (referred to as 24/7 OD in this research). Having multi-year 24/7 OD demand would allow a better understanding of characteristics of dynamic OD demands and their evolution/trends over the past few years, a critical input for modeling transportation system evolution and reliability. This paper presents a data-driven framework that estimates day-to-day dynamic OD using high-granular traffic counts and speed data collected over many years. The proposed framework statistically clusters daily traffic data into typical traffic patterns using t-Distributed Stochastic Neighbor Embedding (t-SNE) and k-means methods. A GPU-based stochastic projected gradient descent method is proposed to efficiently solve the multi-year 24/7 DODE problem. It is demonstrated that the new method efficiently estimates the 5-min dynamic OD demand for every single day from 2014 to 2016 on I-5 and SR-99 in the Sacramento region. The resultant multi-year 24/7 dynamic OD demand reveals the daily, weekly, monthly, seasonal and yearly change in travel demand in a region, implying intriguing demand characteristics over the years. [ABSTRACT FROM AUTHOR]

Shared autonomous vehicles, or SAVs, have attracted significant public and private interest because of their opportunity to simplify vehicle access, avoid parking costs, reduce fleet size, and, ultimately, save many travelers time and money. One way to extend these benefits is through an electric vehicle (EV) fleet. EVs are especially suited for this heavy usage due to their lower energy costs and reduced maintenance needs. As the price of EV batteries continues to fall, charging facilities become more convenient, and renewable energy sources grow in market share, EVs will become more economically and environmentally competitive with conventionally fueled vehicles. EVs are limited by their distance range and charge times, so these are important factors when considering operations of a large, electric SAV (SAEV) fleet. This study simulated performance characteristics of SAEV fleets serving travelers across the Austin, Texas 6-county region. The simulation works in sync with the agent-based simulator MATSim, with SAEV modeling as a new mode. Charging stations are placed, as needed, to serve all trips requested (under 75 km or 47 miles in length) over 30 days of initial model runs. Simulation of distinctive fleet sizes requiring different charge times and exhibiting different ranges, suggests that the number of station locations depends almost wholly on vehicle range. Reducing charge times does lower fleet response times (to trip requests), but increasing fleet size improves response times the most. Increasing range above 175 km (109 miles) does not appear to improve response times for this region and trips originating in the urban core are served the quickest. Unoccupied travel accounted for 19.6% of SAEV mileage on average, with driving to charging stations accounting for 31.5% of this empty-vehicle mileage. This study found that there appears to be a limit on how much response time can be improved through decreasing charge times or increasing vehicle range. [ABSTRACT FROM AUTHOR]

Recent transportation network studies on uncertainty and reliability call for modeling the probabilistic O-D demand and probabilistic network flow. Making the best use of day-to-day traffic data collected over many years, this paper develops a novel theoretical framework for estimating the mean and variance/covariance matrix of O-D demand considering the day-to-day variation induced by travelers’ independent route choices. It also estimates the probability distributions of link/path flow and their travel cost where the variance stems from three sources, O-D demand, route choice and unknown errors. The framework estimates O-D demand mean and variance/covariance matrix iteratively, also known as iterative generalized least squares (IGLS) in statistics. Lasso regularization is employed to obtain sparse covariance matrix for better interpretation and computational efficiency. Though the probabilistic O-D estimation (ODE) works with a much larger solution space than the deterministic ODE, we show that its estimator for O-D demand mean is no worse than the best possible estimator by an error that reduces with the increase in sample size. The probabilistic ODE is examined on two small networks and two real-world large-scale networks. The solution converges quickly under the IGLS framework. In all those experiments, the results of the probabilistic ODE are compelling, satisfactory and computationally plausible. Lasso regularization on the covariance matrix estimation leans to underestimate most of variance/covariance entries. A proper Lasso penalty ensures a good trade-off between bias and variance of the estimation. [ABSTRACT FROM AUTHOR]

This paper reviews the preconditions for successful applications of Experimental Economics methods to research on transportation problems, as new transportation and research technologies emerge. We argue that the application of properly designed incentives, the hallmark of Experimental Economics, provides a high degree of experimental control, leading to internal validity and incentive compatibility. Both of these are essential for ensuring that findings generalize to contexts outside the immediate application. New technologies, such as virtual reality simulators, can generate external validity for the experiments by providing realistic contexts. GPS and other tracking technologies, as well as smart phones, smart cards and connected vehicle technologies can allow detailed observations on actions and real-time interactions with drivers in field experiments. Proper application of these new technologies in research requires an understanding of how to maintain a high level of internal validity and incentive compatibility as external validity is increased. In this review of past applications of Experimental Economics to transportation we focus on their success in achieving external and internal validity. [ABSTRACT FROM AUTHOR]

Traffic data provide the basis for both research and applications in transportation control, management, and evaluation, but real-world traffic data collected from loop detectors or other sensors often contain corrupted or missing data points which need to be imputed for traffic analysis. For this end, here we propose a deep learning model named denoising stacked autoencoders for traffic data imputation. We tested and evaluated the model performance with consideration of both temporal and spatial factors. Through these experiments and evaluation results, we developed an algorithm for efficient realization of deep learning for traffic data imputation by training the model hierarchically using the full set of data from all vehicle detector stations. Using data provided by Caltrans PeMS, we have shown that the mean absolute error of the proposed realization is under 10 veh/5-min, a better performance compared with other popular models: the history model, ARIMA model and BP neural network model. We further investigated why the deep leaning model works well for traffic data imputation by visualizing the features extracted by the first hidden layer. Clearly, this work has demonstrated the effectiveness as well as efficiency of deep learning in the field of traffic data imputation and analysis. [ABSTRACT FROM AUTHOR]

Call detail record (CDR) data from mobile communication carriers offer an emerging and promising source of information for analysis of traffic problems. To date, research on insights and information to be gleaned from CDR data for transportation analysis has been slow, and there has been little progress on development of specific applications. This paper proposes the traffic semantic concept to extract traffic commuters’ origins and destinations information from the mobile phone CDR data and then use the extracted data for traffic zone division. A K -means clustering method was used to classify a cell-area (the area covered by a base stations) and tag a certain land use category or traffic semantic attribute (such as working, residential, or urban road) based on four feature data (including real-time user volume, inflow, outflow, and incremental flow) extracted from the CDR data. By combining the geographic information of mobile phone base stations, the roadway network within Beijing’s Sixth Ring Road was divided into a total of 73 traffic zones using another K -means clustering algorithm. Additionally, we proposed a traffic zone attribute-index to measure tendency of traffic zones to be residential or working. The calculated attribute-index values of 73 traffic zones in Beijing were consistent with the actual traffic and land-use data. The case study demonstrates that effective traffic and travel data can be obtained from mobile phones as portable sensors and base stations as fixed sensors, providing an opportunity to improve the analysis of complex travel patterns and behaviors for travel demand modeling and transportation planning. [ABSTRACT FROM AUTHOR]

The advent of Big Data era has transformed the outlook of numerous fields in science and engineering. The transportation arena also has great expectations of taking the advantage of Big Data enabled by the popularization of Intelligent Transportation Systems (ITS). In this study, the viability of a proactive real-time traffic monitoring strategy evaluating operation and safety simultaneously was explored. The objective is to improve the system performance of urban expressways by reducing congestion and crash risk. In particular, Microwave Vehicle Detection System (MVDS) deployed on an expressway network in Orlando was utilized to achieve the objectives. The system consisting of 275 detectors covers 75 miles of the expressway network, with average spacing less than 1 mile. Comprehensive traffic flow parameters per lane are continuously archived on one-minute interval basis. The scale of the network, dense deployment of detection system, richness of information and continuous collection turn MVDS as the ideal source of Big Data. It was found that congestion on urban expressways was highly localized and time-specific. As expected, the morning and evening peak hours were the most congested time periods. The results of congestion evaluation encouraged real-time safety analysis to unveil the effects of traffic dynamics on crash occurrence. Data mining (random forest) and Bayesian inference techniques were implemented in real-time crash prediction models. The identified effects, both indirect (peak hour, higher volume and lower speed upstream of crash locations) and direct (higher congestion index downstream to crash locations) congestion indicators confirmed the significant impact of congestion on rear-end crash likelihood. As a response, reliability analysis was introduced to determine the appropriate time to trigger safety warnings according to the congestion intensity. Findings of this paper demonstrate the importance to jointly monitor and improve traffic operation and safety. The Big Data generated by the ITS systems is worth further exploration to bring all their full potential for more proactive traffic management. [ABSTRACT FROM AUTHOR]

Oversaturation has become a severe problem for urban intersections, especially the bottleneck intersections that cause queue spillover and network gridlock. Further improvement of oversaturated arterial traffic using traditional mitigation strategies, which aim to improve intersection capacity by merely adjusting signal control parameters, becomes challenging since exiting strategies may (or already) have reached their “theoretical” limits of optimum. Under such circumstance, several novel unconventional intersection designs, including the well-recognized continuous flow intersection (CFI) design, are originated to improve the capacity at bottleneck intersections. However, the requirement of installing extra sub-intersections in a CFI design would increase vehicular stops and, more critically, is unacceptable in tight urban areas with closed spaced intersections. To address these issues, this research proposes a simplified continuous flow intersection (called CFI-Lite) design that is ideal for arterials with short links. It benefits from the CFI concept to enable simultaneous move of left-turn and through traffic at bottleneck intersections, but does not need installation of sub-intersections. Instead, the upstream intersection is utilized to allocate left-turn traffic to the displaced left-turn lane. It is found that the CFI-Lite design performs superiorly to the conventional design and regular CFI design in terms of bottleneck capacity. Pareto capacity improvement for every traffic stream in an arterial system can be achieved under effortless conditions. Case study using data collected at Foothill Blvd in Los Angeles, CA, shows that the new design is beneficial in more than 90% of the 408 studied cycles. The testing also shows that the average improvements of green bandwidths for the synchronized phases are significant. [ABSTRACT FROM AUTHOR]

Traffic surveillance is an important topic in intelligent transportation systems (ITS). Robust vehicle detection is one challenging problem for complex traffic surveillance. In this paper, we propose an efficient vehicle detection method by designing vehicle detection grammars and handling partial occlusion. The grammar model is implemented by novel detection grammars, including structure, deformation and pairwise SVM grammars. First, the vehicle is divided into its constitute parts, called semantic parts , which can represent the vehicle effectively. To increase the robustness of part detection, the semantic parts are represented by their detection score maps. The semantic parts are further divided into sub-parts automatically. The two-layer division of the vehicle is modeled into a grammar model. Then, the grammar model is trained by a designed training procedure to get ideal grammar parameters, including appearance models and grammar productions. After that, vehicle detection is executed by a designed detection procedure with respect to the grammar model. Finally, the issue of vehicle occlusion is handled by designing and training specific grammars. The strategy adopted by our method is first to divide the vehicle into the semantic parts and sub-parts, then to train the grammar productions for semantic parts and sub-parts by introducing novel pairwise SVM grammars and finally to detect the vehicle by applying the trained grammars. Experiments in practical urban scenarios are carried out for complex traffic surveillance. It can be shown that our method adapts to partial occlusion and various challenging cases. [ABSTRACT FROM AUTHOR]

This review discusses some issues related to the use of simulation in transportation analysis. Potential pitfalls are identified and discussed. An overview of some methods relevant to the use of an advanced simulation tool in an optimization context is also provided. [ABSTRACT FROM AUTHOR]

Use of traffic simulation has increased in recent decades; and this high-fidelity modelling, along with moving vehicle animation, has allowed transportation decisions to be made with better confidence. During this time, traffic engineers have been encouraged to embrace the process of calibration, in which steps are taken to reconcile simulated and field-observed performance. According to international surveys, experts, and conventional wisdom, existing (non-automated) methods of calibration have been difficult or inadequate. There has been extensive research on improved calibration methods, but many of these efforts have not produced the flexibility and practicality required by real-world engineers. With this in mind, a patent-pending (US 61/859,819) architecture for software-assisted calibration was developed to maximize practicality, flexibility, and ease-of-use. This architecture is called SASCO (i.e. Sensitivity Analysis, Self-Calibration, and Optimization). The original optimization method within SASCO was based on “directed brute force” (DBF) searching; performing exhaustive evaluation of alternatives in a discrete, user-defined search space. Simultaneous Perturbation Stochastic Approximation (SPSA) has also gained favor as an efficient method for optimizing computationally expensive, “black-box” traffic simulations, and was also implemented within SASCO. This paper uses synthetic and real-world case studies to assess the qualities of DBF and SPSA, so they can be applied in the right situations. SPSA was found to be the fastest method, which is important when calibrating numerous inputs, but DBF was more reliable. Additionally DBF was better than SPSA for sensitivity analysis, and for calibrating complex inputs. Regardless of which optimization method is selected, the SASCO architecture appears to offer a new and practice-ready level of calibration efficiency. [ABSTRACT FROM AUTHOR]

This study addresses guideway network design for personal rapid transit (PRT) favoring transit-oriented development. The guideway network design problem seeks to minimize both the guideway construction cost and users’ travel time. In particular, a set of optional points, known as Steiner points, are introduced in the graph to reduce the guideway length. The model is formulated as a combined Steiner and assignment problem, and a Lagrangian relaxation based solution algorithm is developed to solve the optimal solution. Numerical studies are carried on a real-sized network, and illustrate that the proposed model and solution algorithm can solve the PRT guideway network design problem effectively. [ABSTRACT FROM AUTHOR]

A key issue in solving the difficult bus-bunching problem is being able to have reliable information about the location of the buses in the network. Most advanced public transport systems have buses with GPS devices, but the problem remains of how to send reliable information from the buses to the control unit, particularly when the density of buses is low, but there are high communications reliability requirements. As a solution, we study locating roadside units (RSUs) along the route. The buses, together with the RSUs, form a linear vehicular ad-hoc network (VANET). The RSUs are deployed so to maximize the probability of a vehicle communicating with an RSU in at most two hops. Previous studies on RSU location never took into account two hops, a conceptually different type of network. Rather, they consider that a vehicle is able to communicate only directly to an RSU (one hop), which is a well-known Maximum Covering Problem, in which one of the parties is always immobile , similar to a mobile phone network. Oppositely, our method solves the problem in which two of the intervening parties are mobile and communicate with each other , not possible to solve as a Maximum Covering Problem. We estimate the probability of a vehicle accessing successfully an RSU either directly or through the relay of another vehicle. This probability is later embedded in an integer programming formulation that optimizes the RSU locations for maximum communications likelihood. Numerical examples show that the connection probability is strongly dependent on the coverage ratio of the transmitters and receivers and relatively independent on the vehicle density on the network, when densities are low. Results also show that it is possible to find some cost-efficient solutions which result in a smaller number of RSUs located while assuring a connection probability of 0.9 or higher. [ABSTRACT FROM AUTHOR]

This paper investigates the multimodal network design problem (MMNDP) that optimizes the auto network expansion scheme and bus network design scheme in an integrated manner. The problem is formulated as a single-level mathematical program with complementarity constraints (MPCC). The decision variables, including the expanded capacity of auto links, the layout of bus routes, the fare levels and the route frequencies, are transformed into multiple sets of binary variables. The layout of transit routes is explicitly modeled using an alternative approach by introducing a set of complementarity constraints. The congestion interaction among different travel modes is captured by an asymmetric multimodal user equilibrium problem (MUE). An active-set algorithm is employed to deal with the MPCC, by sequentially solving a relaxed MMNDP and a scheme updating problem. Numerical tests on nine-node and Sioux Falls networks are performed to demonstrate the proposed model and algorithm. [ABSTRACT FROM AUTHOR]

Traffic signals at intersections are an integral component of the existing transportation system and can significantly contribute to vehicular delay along urban streets. The current emphasis on the development of automated (i.e., driverless and with the ability to communicate with the infrastructure) vehicles brings at the forefront several questions related to the functionality and optimization of signal control in order to take advantage of automated vehicle capabilities. The objective of this research is to develop a signal control algorithm that allows for vehicle paths and signal control to be jointly optimized based on advanced communication technology between approaching vehicles and signal controller. The algorithm assumes that vehicle trajectories can be fully optimized, i.e., vehicles will follow the optimized paths specified by the signal controller. An optimization algorithm was developed assuming a simple intersection with two single-lane through approaches. A rolling horizon scheme was developed to implement the algorithm and to continually process newly arriving vehicles. The algorithm was coded in MATLAB and results were compared against traditional actuated signal control for a variety of demand scenarios. It was concluded that the proposed signal control optimization algorithm could reduce the ATTD by 16.2–36.9% and increase throughput by 2.7–20.2%, depending on the demand scenario. [ABSTRACT FROM AUTHOR]

Actuated traffic signal control logic has many advantages because of its responsiveness to traffic demands, short cycles, effective use of capacity leading to and recovering from oversaturation, and amenability to aggressive transit priority. Its main drawback has been its inability to provide good progression along arterials. However, the traditional way of providing progression along arterials, coordinated–actuated control with a common, fixed cycle length, has many drawbacks stemming from its long cycle lengths, inflexibility in recovering from priority interruptions, and ineffective use of capacity during periods of oversaturation. This research explores a new paradigm for traffic signal control, “self-organizing signals,” based on local actuated control but with some additional rules that create coordination mechanisms. The primary new rules proposed are for secondary extensions, in which the green may be held to serve an imminently arriving platoon, and dynamic coordination, in which small groups of closely spaced signals communicate with one another to cycle synchronously with the group’s critical intersection. Simulation tests in VISSIM performed on arterial corridors in Massachusetts and Arizona show overall delay reductions of up to 14% compared to an optimized coordinated–actuated scheme where there is no transit priority, and more than 30% in scenarios with temporary oversaturation. Tests also show that with self-organizing control, transit signal priority can be more effective than with coordinated–actuated control, reducing transit delay by about 60%, or 12 to 14 s per intersection with little impact on traffic delay. [ABSTRACT FROM AUTHOR]

The evacuation operations problem aims to avoid or mitigate the potential loss of life in a region threatened or affected by a disaster. It is shaped to a large extent by the evolution of evacuation traffic resulting from the demand–supply interactions of the associated transportation network. Information-based control is a strategic tool for evacuation traffic operations as it can enable greater access to the affected population and more effective response. However, comparatively few studies have focused on the implementation of information-based control in evacuation operations. This study develops a control module for evacuation operations centered on addressing the demand–supply interactions by using behavior-consistent information strategies. These strategies incorporate the likely responses of evacuees to the information provided in the determination of route guidance information. The control module works as an iterative computational process involving an evacuee route choice model and a control model of information strategies to determine the route guidance information to direct evacuation traffic so as to approach a desired network traffic flow pattern. The problem is formulated as a fuzzy logic based optimization framework to explicitly incorporate practical concerns related to information dissemination characteristics and social equity in evacuation operations. Numerical experiments highlight the importance of accounting for the demand–supply interactions, as the use of behavior-consistent information strategies can lead evacuee route choices to approach the operator-desired proportions corresponding to the desired traffic pattern. The results also indicate that while a behavior-consistent information strategy can be effective, gaps with the desired route proportions can exist due to the discrete nature of the linguistic messages and the real-world difficulty in accurately modeling evacuees’ actual route choice behavior. [ABSTRACT FROM AUTHOR]

The concept of rescheduling is essential to activity-based modeling in order to calculate effects of both unexpected incidents and adaptation of individuals to traffic demand management measures. When collaboration between individuals is involved or timetable based public transportation modes are chosen, rescheduling becomes complex. This paper describes a new framework to investigate algorithms for rescheduling at a large scale. The framework allows to explicitly model the information flow between traffic information services and travelers. It combines macroscopic traffic assignment with microscopic simulation of agents adapting their schedules. Perception filtering is introduced to allow for traveler specific interpretation of perceived macroscopic data and for information going unnoticed; perception filters feed person specific short term predictions about the environment required for schedule adaptation. Individuals are assumed to maximize schedule utility. Initial agendas are created by the FEATHERS activity-based schedule generator for mutually independent individuals using an undisturbed loaded transportation network. The new framework allows both actor behavior and external phenomena to influence the transportation network state; individuals interpret the state changes via perception filtering and start adapting their schedules, again affecting the network via updated traffic demand. The first rescheduling mechanism that has been investigated uses marginal utility that monotonically decreases with activity duration and a monotonically converging relaxation algorithm to efficiently determine the new activity timing. The current framework implementation is aimed to support re-timing, re-location and activity re-sequencing; re-routing at the level of the individual however, requires microscopic travel simulation. [ABSTRACT FROM AUTHOR]

Highlights: [•] Cost functions for matching objects at different times based on direction of travel, distance traveled, and speed. [•] Graph-based tripartite (3D) Hungarian algorithm for tracking vehicles across time frames. [•] Two vanishing points and width (VVW) method for camera calibration that is fully autonomous. [•] Automatic determination of regions of interest and vehicle detection zones. [•] Validation over varied weather conditions and compared with an alternative system. [Copyright &y& Elsevier]

Highlights: [•] Literal ERTMS specifications can be subject to different interpretations. [•] Formal notations are highly recommended when dealing with critical complex systems. [•] A UML class-diagram has been developed to depict ERTMS/ETCS modes and transitions. [•] ETCS modes and transitions' specifications are translated into a formal SMV model. [•] Model-checking technique is shown to be efficient for ETCS specifications analysis. [Copyright &y& Elsevier]

Highlights: [•] Effects of escape area layout in panic escape is examined through ants experiment. [•] Adjustments of layout resulted more than 90% reduction in evacuation time. [•] The results were consistent when scaled up and simulated for humans crowd scenario. [•] This can have implications for developing design solutions for crowd safety. [ABSTRACT FROM AUTHOR]

Highlights: [•] Capacity values presented in handbooks are confirmed by experimental data. [•] Changes of the slope of a stair only have a minor effect on the fundamental diagram. [•] Laboratory experiments lead to slightly higher flow values than field studies. [ABSTRACT FROM AUTHOR]

Highlights: [•] We have introduced the effect of delay in walking in the queueing model. [•] Suitable type of queueing system under various conditions is investigated. [•] It is impossible to obtain our realistic result from the conventional queueing theory. [•] Enhanced queueing systems which reduce the effect of walking distance are proposed. [•] Our results are verified by queueing experiments with real pedestrians. [ABSTRACT FROM AUTHOR]

Abstract: One of the most important aspects for a realistic prediction of pedestrian flows is the modelling of human navigation in normal situations such as early design phases of buildings or transportation systems and hubs as well as in evacuation studies to enhance safety in existing infrastructures. To overcome the limitations of current navigation models, this paper proposes a new hybrid multi-scale model, which closely links information between the small-scale and large-scale navigation layer to improve the navigational behaviour. In the presented hybrid navigation model, graph-based methods using visibility graphs are used to model large-scale wayfinding decisions. The pedestrians’ movements between two nodes of the navigation graph are modelled by means of a dynamic navigation field. The navigation field is updated dynamically during the runtime of the simulation, explicitly considering other pedestrians for determining the fastest path. The proposed hybrid approach provides a realistic modelling of human navigational behaviour and thus a realistic prediction of flows since it reflects the human cognitive processes triggered by wayfinding tasks. This includes taking into account other pedestrians for routing decisions who are visible from the current position of the considered pedestrian. The paper discusses the concept and the technical details of the proposed hybrid multi-scale approach in detail and presents an extensive case study demonstrating its advantages. [Copyright &y& Elsevier]

Highlights: [•] The influence of standing/waiting pedestrians on pedestrian dynamics is investigated empirically and by simulation. [•] We propose a waiting zone model for microscopic simulation of pedestrian dynamics. [•] The model is calibrated and tested using data from video recordings and field experiments. [•] Applying the proposed model adds value to the realism of pedestrian simulations. [•] The model can be applied for analysis and prediction of critical situations. [ABSTRACT FROM AUTHOR]

Highlights: [•] Bluetooth MAC scanner (BMS) communication and data acquisition process is modelled. [•] Errors in BMS data are modelled. [•] Accuracy and reliability of travel time estimates from the BMS data are quantified. [ABSTRACT FROM AUTHOR]

Highlights: [•] A novel ant-based routing algorithm is developed to solve dynamic traffic routing problems in freeway networks. [•] A two-step control strategy, including a network pruning step and the Model Predictive Control approach, is introduced. [•] A simulation study, based on an existing highway network in the Netherlands, is used to illustrate the algorithm. [ABSTRACT FROM AUTHOR]

Highlights: [•] We propose a transportation programming model considering various types of project interdependency. [•] We address the issue of regional balance with an approach based on set-covering formulation. [•] We develop an efficient solution algorithm employing the techniques of Lagrangian Relaxation. [ABSTRACT FROM AUTHOR]

Highlights: [•] A robust winner determination problem for the procurement of transportation services with uncertain shipment volumes is proposed. [•] A 2-stage robust formulation is proposed and solved using a constraint generation algorithm. [•] The proposed approach shows good computational performances. [•] Considerable monetary losses could be avoided when robust solutions are chosen rather than deterministic ones. [ABSTRACT FROM AUTHOR]

Abstract: The majority of field evaluations are focusing on the traffic impact of the control actions. However, with the growing prosperity, consumers demand higher quality transport services, for which reliable transport networks are central. This paper is focused on the evaluation of the ramp metering impact based on both indices: traffic and the travel time reliability on the A6W motorway in Paris. Three strategies are implemented, tested and evaluated. Evaluations include more traditional traffic impacts on one hand and the travel time reliability evaluation on the other which corresponds to the add value of this paper. The results obtained indicate both improvement of the travel time reliability and the average travel time. [Copyright &y& Elsevier]

Abstract: In this paper, we study the global optimality of the multiple-cycle smoothed curve signal timing model that was proposed in Liu et al. (2008) for individual oversaturated intersections. First, we propose a counterexample to show that the recursively decomposing design method in Liu et al. (2008) cannot guarantee the global optimality of multiple-cycle signal timing plan for all traffic scenarios. Second, we give a simple sufficient global optimality condition and show that this condition will usually be satisfied in ordinary traffic scenarios. It indicates that the powerful smoothed curve signal timing model in Liu et al. (2008) is useful in practices. This finding also provides a good starting point to further analyze the performance of oversaturated intersections. [Copyright &y& Elsevier]

Abstract: The problem of optimally locating sensors on a traffic network to measure flows has been object of growing interest in the past few years, due to its relevance in transportation systems. Different locations of sensors on the network can allow, indeed, the collection of data whose usage can be useful for traffic management and control purposes. Many different models have been proposed in the literature as well as corresponding solution approaches. The proposed existing models differ according to different criteria: (i) sensor types to be located on the network (e.g., counting sensors, image sensors, Automatic Vehicle Identification (AVI) readers), (ii) available a-priori information, and (iii) flows of interest (e.g., OD flows, route flows, link flows). The purpose of this paper is to review the existing contributions and to give a unifying picture of these models by categorizing them into two main problems: the Sensor Location Flow-Observability Problem and the Sensor Location Flow-Estimation Problem. For both the problems, we will describe the corresponding computational complexity and the existing results. After describing various models and identifying their advantages and limitations, we conclude with several promising directions for future research and discuss other classes of location problems that address different objectives than the ones reviewed in the paper. [Copyright &y& Elsevier]

Abstract: Data analysis based on the similarity of vehicle trajectories in a vehicular network is emerging as a new exciting paradigm that is important for law enforcement applications (e.g., the analysis of criminal tracking, road traffic security and traffic scheduling). Spatio-temporal data analysis plays an important role in many applications, including transportation infrastructure, border security and inland security. To analyze the moving patterns of vehicles on a road network, a measure for determining the similarity of vehicle trajectories with respect to space and time has to be defined. Although previous research has addressed the trajectory similarity problem, most of the studies focus on Euclidian distance instead of network distance. This paper deals with the variations in applying a spatio-temporal similarity measure with given Points of Interest (POI) and Time of Interest (TOI), treating spatial similarity as a combination of structural and sequence similarities that is evaluated using the techniques of dynamic programming. The similarity set thus formed will be used by the remote database to broadcast trigger-based messages to participating vehicles in a neighborhood for future route- and information-sharing activities. The performance of the scheme is evaluated using experiments on standard real-life data. [Copyright &y& Elsevier]

Abstract: The goal of this article is to provide the theoretical basis for enabling tractable solutions to the “arriving on time” problem and enabling its use in real-time mobile phone applications. Optimal routing in transportation networks with highly varying traffic conditions is a challenging problem due to the stochastic nature of travel-times on links of the network. The definition of optimality criteria and the design of solution methods must account for the random nature of the travel-time on each link. Most common routing algorithms consider the expected value of link travel-time as a sufficient statistic for the problem and produce least expected travel-time paths without consideration of travel-time variability. However, in numerous practical settings the reliability of the route is also an important decision factor. In this article, we consider the following optimality criterion: maximizing the probability of arriving on time at a destination given a departure time and a time budget. We present an efficient algorithm for finding an optimal routing policy with a well bounded computational complexity, improving on an existing solution that takes an unbounded number of iterations to converge to the optimal solution. A routing policy is an adaptive algorithm that determines the optimal solution based on en route travel-times and therefore provides better reliability guarantees than an a priori solution. Novel speed-up techniques to efficiently compute the adaptive optimal strategy and methods to prune the search space of the problem are also investigated. Finally, an extension of this algorithm which allows for both time-varying traffic conditions and spatio-temporal correlations of link travel-time distributions is presented. The dramatic runtime improvements provided by the algorithm are demonstrated for practical scenarios in California. [Copyright &y& Elsevier]

Abstract: In advanced traffic signal control systems, plan transition is the process of switching from one timing plan to another to accommodate changes in traffic demand. In traditional coordinated system operation, whether plans are set by time of day or by traffic responsive plan selection, traffic flow can be severely disrupted by the sudden changes in traffic signal timing. Although various transition methods have been developed and refined, existing transition methods are not based on optimizing operational measures of effectiveness during plan transition periods. As the first step in remedying this situation, this paper presents a non-linear mathematical model that provides constrained delay minimization through incremental and simultaneous adjustments in offset and cycle length during plan transitions. According to the simulation results, currently used transition methods tend to assign an excessive amount of green time to the main street, resulting in additional side street delay without performance improvement for the total transportation network. By contrast, the proposed transition method showed measurable improvement in delay performance under a broad range of traffic and geometric conditions. [Copyright &y& Elsevier]

Abstract: This paper presents a schedule-based dynamic assignment model for transit networks, which takes into account congestion through explicit vehicle capacity constraints. The core of this assignment model is the use of a joint choice model for departure times, stops and runs that defines a space-time path in which users decide to leave at a given time, to access the network at a given stop and to board a given run to reach their destination. The assignment model is defined through a dynamic process approach in which the within-day network loading procedure allocates users on each transit run according to user choice and to the residual capacity of vehicles arriving at stops. The proposed model, albeit general, is specified for frequent users, who constitute a particularly congestion-sensitive class of users. Finally, an application to a real-size test network (part of the Naples transit network in southern Italy) is illustrated in order to test the proposed approach and show the ability of the modelling framework to assess congestion effects on transit networks. [Copyright &y& Elsevier]

Abstract: This paper describes a procedure for solving the bus network design problem and its application in a large urban area (the city of Rome), characterized by: (a) a complex road network topology; (b) a multimodal public transport system (rapid rail transit system, buses and tramways lines); (c) a many-to-many transit demand. The solving procedure consists of a set of heuristics, which includes a first routine for the route generation based on the flow concentration process and a parallel genetic algorithm for finding a sub-optimal set of routes with the associated frequencies. The final goal of the research is to develop an operative tool to support the mobility agency of Rome for the bus network design phase. [Copyright &y& Elsevier]

Abstract: Autonomous vehicles can be used to create realistic simulations of surrounding vehicles in driving simulators. However, the use of autonomous vehicles makes it difficult to ensure reproducibility between subjects. In this paper, an effort is made to solve the problem by combining autonomous vehicles and controlled events. A controlled event can be compared to a theatre play. The aim is to achieve the same initial play conditions for each subject, which can be problematic since the traffic situation around the subject will be dependent upon each subject’s actions while driving in autonomous traffic. This paper presents an algorithm that achieves the transition from autonomous traffic to a predefined start condition for a play. The algorithm has been tested in the Swedish National Road and Transport Research Institute (VTI) driving simulator III with promising results. In most of the cases we examined the algorithm could reconstruct the specified start condition and conduct the transition from autonomous to controlled mode in a inconspicuous way. Some problems were observed regarding moving unwanted vehicles away from the closest area around the simulator vehicle, and this part of the algorithm has to be enhanced. The experiment also showed that the subjects drove faster in the presence of controlled everyday life traffic normally used in the VTI driving simulator than in autonomous traffic. [Copyright &y& Elsevier]

Abstract: 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. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a microscopic model for the study of operations at public transport stops, mainly bus stops and light rail transit stations, from the perspective of the traffic analysis. Firstly, a description of a public transport stop as a traffic mechanism is offered. Then the issue of stop capacity is reviewed, providing typical values of transit system capacities. The most wide-spread analytical model of stop capacity – the Highway Capacity Manual formula – and its components is discussed. Next, a microscopic simulation model as a tool for computing stop capacities and related impacts is described. Results of the calibration and validation of the model are also presented. Two applications are developed to illustrate the model capabilities. The first example deals with a light rail transit station and articulated trams; the second one consists of different classes of buses stopping at a bus stop. It can be concluded from these applications that the proposed model may provide more detailed information about stop operations than other existing approaches. [ABSTRACT FROM AUTHOR]