Network traffic control based on a mesoscopic dynamic flow model

The paper focuses on Network Traffic Control based on aggregate traffic flow variables, aiming at signal settings which are consistent with within-day traffic flow dynamics. The proposed optimisation strategy is based on two successive steps: the first step refers to each single junction optimisation (green timings), the second to network coordination (offsets). Both of the optimisation problems are solved through meta-heuristic algorithms: the optimisation of green timings is carried out through a multi-criteria Genetic Algorithm whereas offset optimisation is achieved with the mono-criterion Hill Climbing algorithm. To guarantee proper queuing and spillback simulation, an advanced mesoscopic traffic flow model is embedded within the network optimisation method. The adopted mesoscopic traffic flow model also includes link horizontal queue modelling. The results attained through the proposed optimisation framework are compared with those obtained through benchmark tools.     

Optimal allocation of turns to lanes at an isolated signal-controlled junction

Conventional design methods require the lane marking patterns, which are painted on ground showing road users the permissible turning directions on different approach lanes, as exogenous inputs to define the traffic stream grouping for analysis. This predefined grouping of traffic movements may restrict the design of signal timings in the optimisation procedures. More recently, a lane-based design method has been developed to relax the lane markings as binary-type control variables in a mathematical programming approach. The lane marking patterns and the signal timings can then be optimised simultaneously in a unified framework. This paper presents an extension work to further relax the numbers of approach lane in traffic arms as new integer variables which can then be optimised to give optimal lane arrangement in various arms of a junction to manage the given traffic demands more efficiently. All well-defined signal timings variables in the phase-based approach as well as the lane marking and lane flow variables in the lane-based approach together with their governing constraints are all preserved in the new formulation for the reserve capacity optimisation of isolated signal-controlled junctions.     

Real-time network-wide traffic signal optimization considering long-term green ratios based on expected route flows

We propose a novel real-time network-wide traffic signal control scheme which is (1) applicable under modern data technologies, (2) flexible in response to variations of traffic flows due to its non-cyclic feature, (3) operable on a network-wide and real-time basis, and (4) capable of considering expected route flows in the form of long-term green time ratios for intersection movement. The proposed system has a two-level hierarchical architecture: (1) strategy level and (2) control level. Considering the optimal states for a long-term period found in the strategy level, the optimal signal timings for a short-term period are calculated in the control level which consists of two steps: (1) queue weight update and (2) signal optimization. Based on the ratio of the cumulative green time to the desired green time is the first step to update the queue weights, which are then used in the optimization to find signal timings for minimum total delay. A parametric queue weight function is developed, discussed and evaluated. Two numerical experiments were given. The first demonstrated that the proposed system performs effectively, and the second shows its capability in a real-world network.     

Sensitivity analysis on stochastic equilibrium transportation networks using genetic algorithm

This study deals with the sensitivity analysis of an equilibrium transportation networks using genetic algorithm approach and uses the bi‐level iterative sensitivity algorithm. Therefore, integrated Genetic Algorithm‐TRANSYT and Path Flow Estimator (GATPFE) is developed for signalized road networks for various level of perceived travel time in order to test the sensitivity of perceived travel time error in an urban stochastic road networks. Level of information provided to drivers correspondingly affects the signal timing parameters and hence the Stochastic User Equilibrium (SUE) link flows. When the information on road system is increased, the road users try to avoid conflicting links. Therefore, the stochastic equilibrium assignment concept tends to be user equilibrium. The GATPFE is used to solve the bi‐level problem, where the Area Traffic Control (ATC) is the upper‐level and the SUE assignment is the lower‐level. The GATPFE is tested for six‐junction network taken from literature. The results show that the integrated GATPFE can be applied to carry out sensitivity analysis at the equilibrium network design problems for various level of information and it simultaneously optimize the signal timings (i.e. network common cycle time, signal stage and offsets between junctions).     

Design and development of a hybrid ant colony-variable neighbourhood search algorithm for a multi-depot green vehicle routing problem

The traditional distribution planning problem in a supply chain has often been studied mainly with a focus on economic benefits. The growing concern about the effects of anthropogenic pollutions has forced researchers and supply chain practitioners to address the socio-environmental concerns. This research study focuses on incorporating the environmental impact on route design problem. In this work, the aim is to integrate both the objectives, namely economic cost and emission cost reduction for a capacitated multi-depot green vehicle routing problem. The proposed models are a significant contribution to the field of research in green vehicle routing problem at the operational level. The formulated integer linear programming model is solved for a set of small scale instances using LINGO solver. A computationally efficient Ant Colony Optimization (ACO) based meta-heuristic is developed for solving both small scale and large scale problem instances in reasonable amount of time. For solving large scale instances, the performance of the proposed ACO based meta-heuristic is improved by integrating it with a variable neighbourhood search.     

Traffic flow on signalized streets

This paper considers a signalized street of uniform width and blocks of various lengths. Its signals are pretimed in an arbitrary pattern, and traffic on it behaves as per the kinematic-wave/variational theory with a triangular fundamental diagram. It is shown that the long run average flow on the street when the number of cars on the street (i.e. the street’s density) is held constant is given by the solution of a linear program (LP) with a finite number of variables and constraints. This defines a point on the street’s macroscopic fundamental diagram. For the homogeneous special case where the block lengths and signal timings are identical, all the LP constraints but one are redundant and the result has a closed form. In this case, the LP recipe matches and simplifies the so-called “method of cuts”. This establishes that the method of cuts is exact for homogeneous problems. However, in the more realistic inhomogeneous case the difference between the two methods can be arbitrarily large.The paper uses the LP method to obtain the macroscopic fundamental diagrams arising under four different traffic coordination schemes for streets with four different block length configurations. It is found that the best scheme depends on the prevailing density. Curiously, the popular scheme in which all the traffic green phases are started synchronously wins only in rare circumstances. Its performance is particularly underwhelming when the street’s blocks are long. The paper also presents density-aware numerical methods to optimize the signal offsets for 1-way and 2-way streets. For 1-way streets operated with a common cycle the method reduces to a simple graphical construction . In this case the resulting flow matches the flow that would arise if all of the street’s intersections except one with the shortest green phase had been eliminated.     

Operational macroscopic modeling of complex urban road intersections

This article describes a new approach to the macroscopic first order modeling and simulation of traffic flow in complex urban road intersections. The framework is theoretically sound, operational, and comprises a large body of models presented so far in the literature.Working within the generic node model class of Tampere et al. (2011), the approach is developed in two steps. First, building on the incremental transfer principle of Daganzo et al. (1997), an incremental node model for general road intersections is developed. A limitation of this model (as of the original incremental transfer principle) is that it does not capture situations where the increase of one flow decreases another flow, e.g., due to conflicts. In a second step, the new model is therefore supplemented with the capability to describe such situations. A fixed-point formulation of the enhanced model is given, solution existence and uniqueness are investigated, and two solution algorithms are developed. The feasibility and realism of the new approach is demonstrated through a synthetic and a real case study.     

Analysis of intersection delay under real-time adaptive signal control

The United States Department of Transportation has recently begun implementation of the national demonstration project for suburban Advanced Traffic Management Systems (ATMS) utilizing the Sydney Coordinated Adaptive Traffic System (SCATS). SCATS is an automated, real time, traffic responsive signal control strategy. The expected benefit from the system comes from its ability to constantly modify signal timing patterns to most effectively accommodate changing traffic conditions. The objectives of this research study were to analyze the differences in certain delay parameters which would occur as a result of implementing SCATS signal control. The study employed a macroscopic simulation procedure to compute intersection delay under both a strategy that changed signal timings once per hour and SCATS signal control. A comparison of delay under both forms of control is presented. The study findings demonstrated mixed results regarding the benefit of SCATS control. A general conclusion of the study was that SCATS distributed the delay across competing approaches more evenly. However, in some cases this resulted in an increase in the total intersection delay. The observed delay change was attributed primarily to the saturation equalization objective of the SCATS control program. SCATS attempts to allocate green time to the intersection approaches based on the degree of saturation. Under this philosophy the system is able to balance the percentage of green time between all approaches, resulting in more uniform delay.     

Optimising the control performance of traffic signals at a single junction

The optimal control performance of a single signal-controlled junction is investigated. Two existing methods for analysing this control problem are discussed. One of these, a combinatorial method, generates all possible control structures in terms of groupings of streams of traffic to have green together and the order in which right of way is granted. The other method allows an existing control structure to be optimised by convex programming techniques. Incompatibilities between these two approaches are illustrated and it is shown that they cannot be combined in a satisfactory manner. A new procedure is framed that allows a control structure generated by the combinatorial method to be optimised directly. This procedure is applied to an example junction to illustrate its use.     

An online speed profile generation approach for efficient airport ground movement

The precise guidance and control of taxiing aircraft based on four-dimensional trajectories (4DTs) has been recognised as a promising means to ensure safe and efficient airport ground movement in the context of ever growing air traffic demand. In this paper, a systematic approach for online speed profile generation is proposed. The aim is to generate fuel-efficient speed profiles respecting the timing constraints imposed by routing and scheduling, which ensures conflict-free movement of aircraft in the planning stage. The problem is first formulated as a nonlinear optimisation model, which uses a more flexible edge-based speed profile definition. A decomposed solution approach (following the framework of matheuristic) is then proposed to generate feasible speed profiles in real time. The decomposed solution approach reduces the nonlinear optimisation model into three tractable constituent problems. The control point arrival time allocation problem is solved using linear programming. The control point speed allocation problem is solved using particle swarm optimisation. And the complete speed profile between control points is determined using enumeration. Finally, improved speed profiles are generated through further optimisation upon the feasible speed profiles. The effectiveness and advantages of the proposed approach are validated using datasets of real-world airports.     

The role of travel demand and network centrality on the connectivity and resilience of an urban street system

In the transportation literature, two major and parallel approaches exist to identify the critical elements of a transportation system. On the one hand, conventional transportation engineering emphasizes travel demand, often in terms of traffic volume (i.e., demand side). On the other hand, newer techniques from Network Science emphasize network topology (i.e., supply side). To better understand the relationship between the two approaches, we first investigate whether they correlate by comparing traffic volume and node centrality. Second, we assess the impact of the two approaches on the connectivity and resilience of a transportation network; connectivity is measured by the relative size of the giant component, and resilience is measured by the network’s adaptive capacity (the amount of extra flow it can handle). The urban road system of Isfahan (Iran) is used as a practical case study. Overall, we find that traffic volume indeed correlates with node centrality. In addition, we find that the weighted degree of a node, i.e., the sum of the capacities of its incident links (for small disruptions) and node betweenness (for large disruptions), best captures node criticality. Nodes with high weighted degree and betweenness should therefore be given higher priority to enhance connectivity and resilience in urban street systems. Regarding link criticality, roads with higher capacities showed a more important role as opposed to betweenness, flow, and congestion.

     

Dynamic green bike repositioning problem – A hybrid rolling horizon artificial bee colony algorithm approach

This paper introduces a new dynamic green bike repositioning problem (DGBRP) that simultaneously minimizes the total unmet demand of the bike-sharing system and the fuel and CO₂ emission cost of the repositioning vehicle over an operational period. The problem determines the route and the number of bikes loaded and unloaded at each visited node over a multi-period operational horizon during which the cycling demand at each node varies from time to time. To handle the dynamic nature of the problem, this study adopts a rolling horizon approach to break down the proposed problem into a set of stages, in which a static bike repositioning sub-problem is solved in each stage. An enhanced artificial bee colony (EABC) algorithm and a route truncation heuristic are jointly used to optimize the route design in each stage, and the loading and unloading heuristic is used to tackle the loading and unloading sub-problem along the route in a given stage. Numerical results show that the EABC algorithm outperforms Genetic Algorithm in solving the routing sub-problem. Computation experiments are performed to illustrate the effect of the stage duration on the two objective values, and the results show that longer stage duration leads to higher total unmet demand and total fuel and CO₂ emission cost. Numerical studies are also performed to illustrate the effects of the weight and the loading and unloading times on the two objective values and the tradeoff between the two objectives.     

TRANSYT traffic engineering program efficiency improvement via Fibonacci search

TRANSYT is a widely-used traffic model developed in Great Britain. Its purpose is to study the traffic behavior of, and to optimize the signal timings of, a network of coordinated signalized intersections. An important subroutine of TRANSYT is an iterative minimization search procedure based on the so-called “hill climbing” strategy. It attempts to minimize a performance index (a weighted combination of stops and delay) by varying the phase lengths and offsets of each signal. The purpose of this paper is to explain how another search procedure, a modification of Fibonacci search, can be used in place of the hill climbing procedure. This results in a significant improvement in TRANSYT, both in terms of the performance index and computational time.     

Bus Priority Using pre-signals

The need to provide efficient public transport services in urban areas has led to the implementation of bus priority measures in many congested cities. Much interest has recently centred on priority at signal controlled junctions, including the concept of pre-signals, where traffic signals are installed at or near the end of a with-flow bus lane to provide buses with priority access to the downstream junction. Although a number of pre-signals have now been installed in the U.K., particularly in London, there has been very little published research into their design, operation and optimisation. This paper addresses these points through the development of analytical procedures which allow pre-implementation evaluation of specific categories of pre-signals. The paper initially sets out three categories of pre-signal, which have different operating characteristics, different requirements for signalling and different impacts on capacity and delay. Key issues concerning signalling arrangements for these categories are then discussed, together with a summary of the analytical approach adopted and the assumptions required. Equations are developed to allow appropriate signal timings to be calculated for pre-signalised intersections. Further equations are then developed to enable delays to priority and non-priority traffic, with and without pre-signals, to be estimated with delay being taken here as the key performance criterion. The paper concludes with three application examples illustrating how the equations are applied and the impacts of pre-signals in different situations.The analyses confirm the potential benefits of pre-signals, where these signals apply to non-priority traffic only. Where buses are also subject to a pre-signal, it is shown that disbenefits to buses can often occur, unless bus detectors are used to gain priority signalling.     

Optimal operation of displaced left-turn intersections: A lane-based approach

Displaced left-turn (DLT) intersections that resolve the conflict between left-turn and opposing-through movements at the pre-signal are probably the most extensively used innovative intersection designs. The DLT intersection concept can be extended to ten different types according to the location of the left-turn transition area, the number of DLT approaches, and the possible setting of the bypass right-turn lane. This paper presents a generalized lane-based optimization model for the integrated design of DLT intersection types, lane markings, the length of the displaced left-turn lane, and the signal timings. The optimization is formulated as a mixed-integer non-linear program. This program is further transformed to a series of mixed-integer linear programming problems that can be solved by the standard branch-and-bound technique. Results from extensive numerical analyses reveal the effectiveness of the proposed method, as well as the promising property of assisting transportation professionals in the proper selection of DLT intersection types, and the design of geometric layout and signal timings.     

A generic class of first order node models for dynamic macroscopic simulation of traffic flows

Node models for macroscopic simulation have attracted relatively little attention in the literature. Nevertheless, in dynamic network loading (DNL) models for congested road networks, node models are as important as the extensively studied link models. This paper provides an overview of macroscopic node models found in the literature, explaining both their contributions and shortcomings. A formulation defining a generic class of first order macroscopic node models is presented, satisfying a list of requirements necessary to produce node models with realistic, consistent results. Defining a specific node model instance of this class requires the specification of a supply constraint interaction rule and (optionally) node supply constraints. Following this theoretical discussion, specific macroscopic node model instances for unsignalized and signalized intersections are proposed. These models apply an oriented capacity proportional distribution of the available supply over the incoming links of a node. A computationally efficient algorithm to solve the node models exactly is included.     

Iterative procedure for equilibrium network traffic signal setting

The Equilibrium Network Traffic Signal Setting problem is an open research area. It can be approached using global optimization models or iterative procedures. In this paper, after a brief review of the state of the art, the main characteristics of the iterative procedure ENETS are described. In this procedure, traffic signal setting is performed in two successive steps: green timing and scheduling at each junction, and signal coordination on the network. Green timing and scheduling at a single junction is based on a mixed-binary linear program with capacity factor maximization. Signal coordination for the whole network is performed by solving a discrete programming model with total delay minimization. The flow assignment stage refers to the separable user equilibrium model with fixed demand, and uses a feasible direction algorithm, which can also be adopted to cover the cases of elastic demand and/or asymmetric equilibrium. An experimental test of ENETS on a small network and a graphical explanation of the procedure are described and discussed.     

A method to estimate trip O‐D patterns using a neural network approach

This paper proposes a method which identifies the trip origin‐destination (O‐D) matrix when many pairs of values for the right hand side column (B) and the bottom row (A) of the matrix are given. The method considers B and A as the cause (input) and effect (output) of a system, respectively, and that the O‐D matrix represents the relationship between the cause and the effect. The relationship which satisfies all pairs of the cause and the effect data exactly may not be identified, but, should a general pattern of the relationship exist, it should emerge when many data sets of B and A are given. Two steps are involved in the method: the first step examines if a consistent O‐D pattern exists; if a pattern is found to exist, the second step identifies the values of the elements of the O‐D matrix. The first step is based on the shape of the possibility distributions of the values of the matrix elements. The second step uses a simple back‐propagation neural network. The method is useful to problems that require identification of the cause‐effect relationship when many sets of data for the cause and effect are available, for example, the station‐to‐station travel pattern on a rapid transit line when the total entering and exiting passengers are known at each station for many different days. The model can also be applied to other transportation problems which involve input and output relation.     

Multi-criteria optimization of traffic signals: Mobility,safety, and environment

Two-dimensional multi-objective optimizations have been used for decades for the problems in traffic engineering although only few times so far in the optimization of signal timings. While the other engineering and science disciplines have utilized visualization of 3-dimensional Pareto fronts in the optimization studies, we have not seen many of those concepts applied to traffic signal optimization problems. To bridge the gap in the existing knowledge this study presents a methodology where 3-dimensional Pareto Fronts of signal timings, which are expressed through mobility, (surrogate) safety, and environmental factors, are optimized by use of an evolutionary algorithm. The study uses a segment of 5 signalized intersections in West Valley City, Utah, to test signal timings which provide a balance between mobility, safety and environment. In addition, a set of previous developed signal timing scenarios, including some of the Connected Vehicle technologies such as GLOSA, were conducted to evaluate the quality of the 3-dimensional Pareto front solutions. The results show success of 3-dimensinal Pareto fronts moving towards optimality. The resulting signal timing plans do not show large differences between themselves but all improve on the signal timings from the field, significantly. The commonly used optimization of standard single-objective functions shows robust solutions. The new set of Connected Vehicle technologies also shows promising benefits, especially in the area of reducing inter-vehicular friction. The resulting timing plans from two optimization sets (constrained and unconstrained) show that environmental and safe signal timings coincide but somewhat contradict mobility. Further research is needed to apply similar concepts on a variety of networks and traffic conditions before generalizing findings.     

Optimisation of motorway operations via ramp metering and variable speed limits

This paper presents a modelling and optimisation framework for deriving ramp metering and variable speed control strategies. We formulate the optimal control problems aiming to minimise the travel delay on motorways based upon a macroscopic cell transmission model of traffic. The optimal ramp metering optimisation is formulated as a linear programming (LP) while the variable speed control problem is formulated as a mixed integer LP. The optimisation models are applied to a real scenario over a section of M25 motorway in the UK. This paper also includes various analyses on the sensitivity of the optimal control solutions with respect to different network configurations and model assumptions.