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Vehicle movements at signalized intersections should follow the guidance of lane marking arrows. Turns are permitted or banned depending on the existence of lane marking arrows establishing network link connectivity. Lane markings are the interface that joins consecutive upstream and downstream intersections. Traffic flows from origins to destinations across intersections should be governed by lane markings. In this study, conventionally fixed lane markings are relaxed as binary variables to be optimized by maximizing the green bandwidths. The proposed methodology is innovative in that it extends the lane-based design framework to incorporate green band maximization for enhancing traffic signal coordination. Path flows are controlled to satisfy flow conservations and to compile turning flows at intersections. With these turning flows as inputs, lane markings can be optimized together with the coordinated traffic signal settings. Path flows, path travel times, and path choices are evaluated through new linear constraints. For path travel times, cruise times along lanes and total delays at the ends of lanes are evaluated. The non-linear total delays are approximated by the proposed linearized delay function. The model coefficients are calibrated by network data as in a previous study. The problem is formulated as a binary-mixed-integer-linear-program and solved by standard branch-and-bound techniques using a CPLEX solver. To avoid non-linearity in the problem formulation, the bandwidth maximization approach is adopted instead of minimizing the total network delay in the design of the signalized network. A network with four intersections is provided to demonstrate how optimized lane markings can create efficient network link configurations. The numerical results are promising compared with those obtained in previous studies and show that the overall network performance can be improved. [ABSTRACT FROM AUTHOR]

In developing nations, many expanding cities are facing challenges that result from the overwhelming numbers of people and vehicles. Collecting real-time, reliable and precise traffic flow information is crucial for urban traffic management. The main purpose of this paper is to develop an adaptive model that can assess the real-time vehicle counts on urban roads using computer vision technologies. This paper proposes an automatic real-time background update algorithm for vehicle detection and an adaptive pattern for vehicle counting based on the virtual loop and detection line methods. In addition, a new robust detection method is introduced to monitor the real-time traffic congestion state of road section. A prototype system has been developed and installed on an urban road for testing. The results show that the system is robust, with a real-time counting accuracy exceeding 99% in most field scenarios. [ABSTRACT FROM AUTHOR]

This paper presents the use of the Aimsun microsimulation program to simulate vehicle violating behaviors and observe their impact on road traffic crash risk. Plugins for violations of speeding, slow driving, and abrupt stopping were developed using Aimsun’s API and SDK module. A safety analysis plugin for investigating probability of rear-end collisions was developed, and a method for analyzing collision risk is proposed. A Fuzzy C-mean Clustering algorithm was developed to identify high risk states in different road segments over time. Results of a simulation experiment based on the G15 Expressway in Shanghai showed that abrupt stopping had the greatest impact on increasing collision risk, and the impact of violations increased with traffic volume. The methodology allows for the evaluation and monitoring of risks, alerting of road hazards, and identification of hotspots, and could be applied to the operations of existing facilities or planning of future ones. [ABSTRACT FROM AUTHOR]

Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed) collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN), two learning processes are proposed: (1) a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2) a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE), root mean square error (RMSE), and mean absolute relative error (MARE) are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR), instantaneous model (IM), linear model (LM), neural network (NN), and cumulative plots (CP). [ABSTRACT FROM AUTHOR]

Abstract: The traditional trip-based approach to transportation modeling has been employed for the past decade. The last step of the trip-based modeling approach is traffic assignment, which has been typically formulated as a user equilibrium (UE) problem. In the conventional perspective, the definition of UE traffic assignment is the condition that no road user can unilaterally change routes to reduce their travel time. An equivalent definition is that the travel times of all the used paths between any given origin–destination pair are equal and less than those of the unused paths. The underlying assumption of the UE definition is that road users have full information on the available transportation paths and can potentially use any path if the currently used path is overly congested. However, a more practical scenario is that each road user has a limited path set within which she/he can choose routes from. In this new scenario, we call the resulting user equilibrium an N-path user equilibrium (NPUE), in which each road user has only N paths to select from when making route choices in the network. We introduce a new formulation of the NPUE and derive optimality conditions based on this formulation. Different from traditional modeling framework, the constraints of the proposed model are of linear form, which makes it possible to solve the problem with conventional convex programming techniques. We also show that the traditional UE is a special case of an NPUE and prove the uniqueness of the resulting flow pattern of the NPUE. To efficiently solve this problem, we devise path-based and link-based solution algorithms. The proposed solution algorithms are empirically applied to networks of various sizes to examine the impact of constrained user path sets. Numerical results demonstrate that NPUE results can differ significantly from UE results depending on the number of paths available to road users. In addition, we observed an interesting phenomenon, where increasing the number of paths available to road users can sometimes decrease the overall system performance due to their selfish routing behaviors. This paradox demonstrates that network information should be provided with caution, as such information can do more harm than good in certain transportation systems. [Copyright &y& Elsevier]

The increased interest in the development and application of large-scale or regional microsimulation transportation models has brought to the forefront the challenges associated with estimating the dynamic demand information needed to run such models. This paper develops a computational-based approach for estimating or adjusting dynamic origin-destination matrices for regional microsimulation models on the basis of hourly traffic counts. The proposed approach, while based on genetic algorithms (GA), includes a special module, called Plan Analyzer, to guide the search process in an intelligent way. This results in a customized algorithm for the problem that can be regarded as an example of a guided genetic algorithm (GGA). To cut down on execution time, a distributed implementation of the algorithm is adopted, and several software design procedures are developed to deal with the demanding memory requirements of the problem. To demonstrate the effectiveness of the algorithm, the Transportation Analysis and Simulation System (TRANSIMS) model, a microsimulation platform designed for regional simulations, is used to model two test networks, a synthetic grid network and a realistic regional model of Chittenden County, Vermont. The GGA is then utilized to estimate the dynamic demand for those two models on the basis of hourly traffic count information. The results clearly demonstrate the effectiveness of the GGA in dramatically reducing the average absolute error (AAE) between the simulated and field counts, and in closely estimating the 'true' demand, which was known in this research by virtue of how the case studies were designed. The results also show that the developed GGA significantly outperforms standard GAs. [ABSTRACT FROM AUTHOR]

Abstract: This paper develops a formulation for the network level dynamic traffic equilibrium model with departure time choice and route choice. The embedded network loading procedure follows the cell transmission model without the holding-back issues by using detailed representations of flows at merges and diverges. The problem is modeled using a complementarity approach. The existence of the equilibrium solution is discussed using techniques from generalized variational inequalities. Computational results are performed using state of the art solvers. Since these solvers fail to solve any reasonable size networks, a specialized projection algorithm is developed to solve the problem. Numerical results are presented to demonstrate the performance of the algorithm in various starting with simple networks and extending to reasonable size networks with different traffic parameters. It is shown that the solution procedure produces good dynamic equilibrium solutions for general transportation networks. [Copyright &y& Elsevier]

Abstract: In transportation subnetwork–supernetwork analysis, it is well known that the origin–destination (O–D) flow table of a subnetwork is not only determined by trip generation and distribution, but also a result from traffic routing and diversion, due to the existence of internal–external, external–internal and external–external flows. This result indicates the variable nature of subnetwork O–D flows. This paper discusses an elastic O–D flow table estimation problem for subnetwork analysis. The underlying assumption is that each cell of the subnetwork O–D flow table contains an elastic demand function rather than a fixed demand rate and the demand function can capture all traffic diversion effect under various network changes. We propose a combined maximum entropy-least squares estimator, by which O–D flows are distributed over the subnetwork in terms of the maximum entropy principle, while demand function parameters are estimated for achieving the least sum of squared estimation errors. While the estimator is powered by the classic convex combination algorithm, computational difficulties emerge within the algorithm implementation until we incorporate partial optimality conditions and a column generation procedure into the algorithmic framework. Numerical results from applying the combined estimator to a couple of subnetwork examples show that an elastic O–D flow table, when used as input for subnetwork flow evaluations, reflects network flow changes significantly better than its fixed counterpart. [Copyright &y& Elsevier]

Path flow estimator (PFE) is a single-level network observer proposed to estimate path flows and origin-destination flows from traffic counts in a transportation network. The PFE model handles the traffic count inconsistency problem by allowing user to specify appropriate error bounds (or confidence interval) on the traffic counts. This approach, although flexible, introduces a systematic bias in underestimating the total demand when improper error bounds are specified. This paper presents an L∞-norm PFE model that minimizes the systematic bias of the total demand estimate encountered in the PFE model by determining the least maximum absolute error needed to accommodate measurement errors and traffic count inconsistencies within the estimation process. A solution algorithm based on the dual formulation combined with a column generation procedure is developed for solving the proposed L∞-norm PFE model. Numerical results are presented to illustrate the features and applicability of the proposed L∞-norm PFE model and solution algorithm. [ABSTRACT FROM AUTHOR]

Traffic-responsive plan selection (TRPS) control is one of the largely underutilized control features in National Electircal Manufacturers Association traffic controllers. This control feature allows the controller to change timing plans in arterials or networks in response to changes in traffic patterns automatically, and can therefore result in better performance of the signal system. However, there is limited number of studies that document the procedure of TRPS configuration. The authors have previously published general guidelines on TRPS system setup and configuration, laying out a comprehensive approach that assumes general traffic patterns, a standardized arterial network, and some generic system detector locations. This paper fills a significant gap in literature by providing an illustrative procedure of actual field implementation of a TRPS system in Mexia, Texas. The paper demonstrates the configuration methodology on an arterial network that does not conform to the standardized guidelines. This implementation resulted in up to 75% reduction in delay and up to 33% increase in the lower arterial speed. In this paper, the authors provide some findings and insights into the TRPS configuration process, highlighting some solutions for potential field problems that the engineer might face when configuring a TRPS control system. [ABSTRACT FROM AUTHOR]

Abstract: Path flow estimator (PFE) is a one-stage network observer proposed to estimate path flows and hence origin–destination (O–D) flows from traffic counts in a transportation network. Although PFE does not require traffic counts to be collected on all network links when inferring unmeasured traffic conditions, it does require all available counts to be reasonably consistent. This requirement is difficult to fulfill in practice due to errors inherited in data collection and processing. The original PFE model handles this issue by relaxing the requirement of perfect replication of traffic counts through the specification of error bounds. This method enhances the flexibility of PFE by allowing the incorporation of local knowledge, regarding the traffic conditions and the nature of traffic data, into the estimation process. However, specifying appropriate error bounds for all observed links in real networks turns out to be a difficult and time-consuming task. In addition, improper specification of the error bounds could lead to a biased estimation of total travel demand in the network. This paper therefore proposes the norm approximation method capable of internally handling inconsistent traffic counts in PFE. Specifically, three norm approximation criteria are adopted to formulate three L p-PFE models for estimating consistent path flows and O–D flows that simultaneously minimize the deviation between the estimated and observed link volumes. A partial linearization algorithm embedded with an iterative balancing scheme and a column generation procedure is developed to solve the three L p-PFE models. In addition, the proposed L p-PFE models are illustrated with numerical examples and the characteristics of solutions obtained by these models are discussed. [Copyright &y& Elsevier]

Several analytic approaches have been developed to describe or predict traffic flows on networks with time-varying (dynamic) travel demands, flows and travel times. A key component of these models lies in modelling the flows and/or travel times on the individual links, but as this is made more realistic or accurate it tends to make the overall model less computationally tractable. To help overcome this, and for other reasons, we develop a bi-level user equilibrium (UE) framework that separates the assignment or loading of flows on the time-space network from the modelling of flows and trip times within individual links. We show that this model or framework satisfies appropriate definitions of UE satisfies a first-in-first-out (FIFO) property of road traffic, and has other desirable properties. The model can be solved by iterating between (a) a linear network- loading model that takes the lengths of time-space links as fixed (within narrow ranges), and (b) a set of link flow sub-models which update the link trip times to construct a new time-space network. This allows links to be processed sequentially or in parallel and avoids having to enumerate paths and compute path flows or travel times. We test and demonstrate the model and algorithms using example networks and find that the algorithm converges quickly and the solutions behave as expected. We show how to extend the model to handle elastic demands, multiple destinations and multiple traffic types, and traffic spiliback within links and from link to link. [ABSTRACT FROM AUTHOR]

Recent years have witnessed an interest in increasing the resolution of transportation models to allow them to better address emerging transportation issues, such as concerns about emissions, air quality, and emergency preparedness. Microscopic simulation models, especially those that combine routing logic with accurate modeling of traffic flow dynamics, have vast untapped potential for modeling large-scale transportation networks and for achieving the resolution level required for addressing such emerging issues. Building and calibrating such models, however, is quite challenging and is currently not widely understood. This paper describes the process of developing and calibrating a large-scale microscopic model of Chittenden County, Vt., an area of about 1,400 km2 (540 mi2). The model was developed primarily using readily available data to which most metropolitan planning organizations are expected to have access. Following a brief description of how the network was coded and the traffic demand specified, the paper discusses the process of error checking and model calibration. Preliminary calibration results are encouraging, given the complexity of the model. The paper includes a summary of the main lessons learned and modeling pitfalls that should be avoided. [ABSTRACT FROM AUTHOR]

Abstract: This paper concerns a frequency-based route choice model for congested transit networks, which takes into account the consequences of congestion on the predicted flows as well as on the expected waiting and travel times. The paper builds on the results presented in Correa [Correa, J., 1999. Asignación de flujos de pasajeros en redes de transporte público congestionadas. Engineering thesis, U. de Chile, Santiago] and Cominetti and Correa [Cominetti, R., Correa, J., 2001. Common-lines and passenger assignment in congested transit networks. Transportation Science 35(3), 250–267], extending these to obtain a new characterization of the equilibria which allows us to formulate an equivalent optimization problem in terms of a computable gap function that vanishes at equilibrium. This new model formulation can deal with flow dependent travel times and is a generalization of the previously known strategy (hyperpath) based transit network equilibrium models. The approach leads to an algorithm which has been applied successfully on large scale networks. Computational results for transit networks originating from practice demonstrate the applicability of the proposed approach. [Copyright &y& Elsevier]

Presents abstracts of several studies on environment and planning. "Explaining the Growth of British Multiple Retailing During the Golden Age," by Carlo Morelli; "Cities in the Shade: Urban Geography and the Uses of Noir," by Matthew Farish; "Geographers and the Tennessee Valley Authority," by Ronald Reed Boyce.

Daily traffic assignments to a large-scale road network are described for build and no-build scenarios to evaluate the addition of two proposed ramps between I-295 and SR-42 in the New Jersey part of the Delaware Valley region. The road network consists of 39,800 links connecting 1,510 zones. The user-equilibrium traffic-assignment problem was solved with a new algorithm called origin-based assignment (OBA), which can achieve highly converged solutions with reasonable computing effort. Following a description of the user-equilibrium traffic-assignment problem and the OBA algorithm, the stability of link-flow differences between the two scenarios in the vicinity of the proposed ramps are examined over a broad range of assignment convergence levels. Then, link-flow differences over this range of convergence levels are compared to link-flow differences between two very highly converged solutions. Examination of the findings reveals, in the writers’ view, that a relative gap of 0.01% (0.0001) is required to ensure that the traffic assignments are sufficiently converged to achieve link-flow stability. These convergence levels are then interpreted in terms of the number of Frank-Wolfe iterations needed to achieve comparable relative gaps as well as the computational effort required. [ABSTRACT FROM AUTHOR]

The problem considered is to locate one new facility with respect to a finite number of pars of existing facilities on a tree graph, which typically represents a road network, so as to minimize the maximum cost, where costs are linear increasing functions of the round-trip distance a vehicle based at the new facility travels via a pair of existing facilities. Based on an attainable lower bound for the minimax problem, an algorithm is developed that yields all optimal solutions to the problem. [ABSTRACT FROM AUTHOR]

In this paper we present a version of the (static) traffic equilibrium problem in which the cost incurred on each path is not simply the sum of the costs on the arcs that constitute that path. We motivate this nonadditive version of the problem by describing several situations in which the classic additivity assumption fails. We describe existence and uniqueness conditions for this problem, and we also present convergence theory for a generic algorithm for solving nonadditive problems. [ABSTRACT FROM AUTHOR]

We consider a problem in which a fleet of vehicles must be scheduled to pickup and deliver a set of orders in truckload quantities. We describe a new algorithm based on a network flow relaxation which imposes necessary conditions on the flow of empty vehicles from order delivery points to order pickup points. The network flow model provides a lower bound and a nearly feasible solution that can be made feasible with some simple heuristics. Our algorithm is fast and has performed well on a set of more than 430 test problems which include a number of real problems obtained from the Shanghai Truck Transportation Corporation. On real problems and on random problems generated using real pickup and delivery points, the algorithm consistently produces solutions within 1% of optimality. [ABSTRACT FROM AUTHOR]

The article presents a list of selected abstracts for the 1990-1991 "Transportation Science Section" dissertation prize competition. Fourteen dissertations were submitted for this competition. Jury members were asked to read all dissertations. The abstract "Dynamic Networks with Random Arc Capacities, with Application to the Stochastic Dynamic Vehicle Allocation Problem," by Linos F. Frantzeskakis addresses the Dynamic Vehicle Allocation (DVA) problem in an uncertain demand environment. It involves the management of a vehicle fleet over time, with the objective of maximizing the accrued profits over a time horizon of N periods. The abstract "Characterization of Crowd Behavior and Movement," by Saad A. H. Al-Gahdi deals mainly with steady state flows under normal conditions. Scaling Algorithms for Multicommodity Flow Problems and Network Flow Problems with Side Constraints," by Rina Schneur focuses on scaling techniques that have led to efficient algorithms for pure network flow problems.

This paper discusses a family of general link cost functions that can be used to model multimodal transportation network equilibrium problems. The family includes as a special case the currently favored family of monotonically increasing functions but does not necessarily have to have a symmetric or semi-definite Jacobian. In this way multimodal networks can be modeled somewhat more realistically. The paper also allows stochastic link costs for some or all the links and modes. It is shown that under mild conditions the equilibrium exists and is unique, but more importantly, that there is a simple, single-equilibrium algorithm that is proven to converge for stochastic networks with the link cost family discussed in the paper. Finally, it is pointed out that many variable demand problems also exhibit unique equilibria, but that there are some which do not. An example which illustrates this fact is given. [ABSTRACT FROM AUTHOR]

In a noncongested transportation network where each user chooses his quickest route, the creation of an additional route between some origin-destination pair clearly cannot result in an increase in travel time to users traveling between that o-d pair. It seems reasonable to assume the same can be said of congested networks. In 1968, D. Braess presented a remarkable example demonstrating this is not the case: a new route can increase travel time for all. The present paper gives, under reasonable assumptions, necessary and sufficient conditions for "Braess' Paradox" to occur in a general transportation network. As a corollary, we obtain that Braess' Paradox is about as likely to occur as not occur. [ABSTRACT FROM AUTHOR]

This paper analyzes the substructure of linear programming tableaux for capacitated transshipment problems. Specifically, it demonstrates that submatrices of these tableaux correspond in a precise way to certain "contractions" of the transshipment network. The results are couched within the GNET/Depth network data structure of Bradley, Brown and Graves or the predecessor/thread/distance structure of Glover, Klingman, et al. An application to fixed-charge network problems is described. [ABSTRACT FROM AUTHOR]

The general problem is that of locating a central facility in a network so as to minimize the sum of its distances from the sources of flow to it, each distance being appropriately weighted to reflect the associated flow volume and/or cost. In this paper, simple one-pass solution algorithms are given for two classes of topologically simple networks, namely those which are either acyclic or contain exactly one cycle. The first algorithm is based on a reduction procedure that may also yield useful simplification of problems involving general networks. [ABSTRACT FROM AUTHOR]

This paper proposes an algorithm for making a sensitivity analysis on a flow network, specifically, the problem of removing n arcs in a network such that the reduction in the maximum flow between an origin and destination node is maximized. Such an algorithm might be helpful in determining how sensitive a transportation system is to having its roads closed for repairs. [ABSTRACT FROM AUTHOR]

A linear control algorithm is proposed for the computer operation of a signalized traffic intersection. A theoretical analysis of the algorithm under the assumption of constant vehicular arrival and departure rates shows that the control is stable under unsaturated conditions, and with proper choice of control constants usually gives a rapid convergence to a limit cycle that minimizes the average delay. [ABSTRACT FROM AUTHOR]

Microscopic traffic simulators have become efficient tools to conduct different analytic studies on roads, vehicles, behavior of drivers, and critical intersections, which lead towards a well-planned traffic solution. Devising a realistic and sustainable traffic solution requires replication of the real traffic scenario in a simulator. For example, to simulate the traffic stream of less developed countries, we need to simulate non-lane based heterogeneous traffic stream, i.e., motorized and non-motorized vehicles, with their on road behaviors such as irregular pedestrian, illegal parking, violation of laws pertaining lanes, etc. However, most of the existing traffic simulators are unable to mimic the unstructured road traffic stream of less developed countries with their diversified behaviors. We also need to represent the corresponding geographical maps as a list of nodes and edges with relevant geographic information such as length and width of the edges to feed the simulator. However, we lack a Geographic Information System (GIS) protocol to construct an effective simulator readable road network from authoritative polygonal GIS maps. State-of-the-art GIS protocol often converts the geographical maps using polyline data collected from less-credible OpenStreetMap. Although polyline data has length information (for example, length of a street), it lacks width information limiting its capability of being an effective simulator readable road network map. Therefore, in this work, we propose a new microscopic traffic simulator to handle unstructured road traffic stream of less developed countries with their diversified behaviors. To feed the simulator, we also propose a novel parsing algorithm to construct an effective simulator-readable road network from GIS maps through extracting both length and width information of the edges and develop parser based on the algorithm. Our parsing algorithm realizes a delicate blending of geometry with computation over road points to eventually generate the simulator-readable road network. Our simulator receives the network topology generated by our parser, traffic routes, and traffic demand flow rates as input, visualizes the traffic flows, and provides relevant traffic statistics. To evaluate sustainability of our proposed parser and simulator in real-life scenarios, we evaluate our parser by parsing GIS street maps of four different areas resulting in 86% accuracy on an average and evaluate our simulator after necessary calibration using real traffic data resulting in 99% accuracy in terms of travel time. [ABSTRACT FROM AUTHOR]