Multi-objective signal control of urban junctions – Framework and a London case study

This paper presents an approach to multi-objective signal control using fuzzy logic. The signal control uses fuzzy logic where the membership functions are optimised according to the Bellman–Zadeh principle of fuzzy decision-making. This approach is both practical for the decision-maker and efficient, as it leads directly to a Pareto-optimal solution. Signal control priorities are ultimately a political decision. Therefore the tool developed in this research allows the traffic engineer to balance the objectives easily by setting acceptability and unacceptability thresholds for each objective. Particular attention is given in the example to pedestrian delays. The membership functions of the fuzzy logic are optimised by a genetic algorithm coupled to the VISSIM microscopic traffic simulator. The concept is illustrated with a case study of the Marylebone Road–Baker Street intersection in London at which pedestrians as well as vehicle flows are high. The results prove the feasibility of the framework and show the vehicle delays for a more pedestrian friendly signal control strategy.     

An intersection turning movement estimation procedure based on path flow estimator

Estimation of intersection turning movements is one of the key inputs required for a variety of transportation analysis, including intersection geometric design, signal timing design, traffic impact assessment, and transportation planning. Conventional approaches that use manual techniques for estimation of turning movements are insensitive to congestion. The drawbacks of the manual techniques can be amended by integrating a network traffic model with a computation procedure capable of estimating turning movements from a set of link traffic counts and intersection turning movement counts. This study proposes using the path flow estimator, originally used to estimate path flows (hence origin–destination flows), to derive not only complete link flows, but also turning movements for the whole road network given some counts at selected roads and intersections. Two case studies using actual traffic counts are used to demonstrate the proposed intersection turning movement estimation procedure. Copyright © 2010 John Wiley & Sons, Ltd.     

Model based urban traffic control,part I: Local model and local model predictive controllers

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.     

Development of a delay model for unsignalized intersections applicable to traffic assignment

Abstract

This paper develops a model for estimating unsignalized intersection delays which can be applied to traffic assignment (TA) models. Current unsignalized intersection delay models have been developed mostly for operational purposes, and demand detailed geometric data and complicated procedures to estimate delay. These difficulties result in unsignalized intersection delays being ignored or assumed as a constant in TA models.

Video and vehicle license plate number recognition methods are used to collect traffic volume data and to measure delays during peak and off-peak traffic periods at four unsignalized intersections in the city of Tehran, Iran. Data on geometric design elements are measured through field surveys. An empirical approach is used to develop a delay model as a function of influencing factors based on 5- and 15-min time intervals. The proposed model estimates delays on each approach based on total traffic volumes, rights-of-way of the subject approach and the intersection friction factor. The effect of conflicting traffic flows is considered implicitly by using the intersection friction factor. As a result, the developed delay model guarantees the convergence of TA solution methods.

A comparison between delay models performed using different time intervals shows that the coefficients of determination, R ², increases from 43.2% to 63.1% as the time interval increases from 5- to 15-min. The US Highway Capacity Manual (HCM) delay model (which is widely used in Iran) is validated using the field data and it is found that it overestimates delay, especially in the high delay ranges.     

Integrated fuzzy signal and ramp‐metering at a diamond interchange

We propose a fuzzy logic control for the integrated signal operation of a diamond interchange and its ramp meter, to improve traffic flows on surface streets and motorway. This fuzzy logic diamond interchange (FLDI) comprises of three modules: fuzzy phase timing (FPT) module that controls the green time extension of the current phase, phase logic selection (PLS) module that decides the next phase based on the pre‐defined phase sequence or phase logic and, fuzzy ramp‐metering (FRM) module that determines the cycle time of the ramp meter based on current traffic volumes and conditions of the surface streets and the motorways. The FLDI is implemented in Advanced Interactive Microscopic Simulator for Urban and Non‐Urban Network Version 6 (AIMSUN 6), and compared with the traffic actuated signal control. Simulation results show that the FLDI outperforms the traffic‐actuated models with lower system total travel time, average delay, and improvements in downstream average speed and average delay. Copyright © 2011 John Wiley & Sons, Ltd.     

Real-time queue length estimation for congested signalized intersections

How to estimate queue length in real-time at signalized intersection is a long-standing problem. The problem gets even more difficult when signal links are congested. The traditional input–output approach for queue length estimation can only handle queues that are shorter than the distance between vehicle detector and intersection stop line, because cumulative vehicle count for arrival traffic is not available once the detector is occupied by the queue. In this paper, instead of counting arrival traffic flow in the current signal cycle, we solve the problem of measuring intersection queue length by exploiting the queue discharge process in the immediate past cycle. Using high-resolution “event-based” traffic signal data, and applying Lighthill–Whitham–Richards (LWR) shockwave theory, we are able to identify traffic state changes that distinguish queue discharge flow from upstream arrival traffic. Therefore, our approach can estimate time-dependent queue length even when the signal links are congested with long queues. Variations of the queue length estimation model are also presented when “event-based” data is not available. Our models are evaluated by comparing the estimated maximum queue length with the ground truth data observed from the field. Evaluation results demonstrate that the proposed models can estimate long queues with satisfactory accuracy. Limitations of the proposed model are also discussed in the paper.     

“互联网+”交通信号辅助优化机制探索

为优化城市道路交通信号控制方法,本文结合交通信号控制系统建设发展现状,分析当前各大城市交通信号控制系统普遍存在的问题,立足于互联网环境下的浮动车数据,提出基于互联网平台大数据的交通信号控制辅助优化机制。研究发现可利用互联网路口拥堵报警数据及时有效发现问题路口,利用路段拥堵指数及路口交通流参数变化趋势辅助评估配时方案的优化效果,并通过成都市应用实例证明该机制适用于当前交通控制场景需求,可有效辅助交通信号优化工作,是传统交通模式向真正智能交通模式过渡的阶梯。     

Model based urban traffic control,part II: Coordinated model predictive controllers

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.     

Behavior-consistent real-time traffic routing under information provision

The problem addressed here involves a controller seeking to enhance traffic network performance via real-time routing information provision to drivers while explicitly accounting for drivers’ likely reactions towards the information. A fuzzy control modeling approach is used to determine the associated behavior-consistent information-based network control strategies. Experiments are performed to compare the effectiveness of the behavior-consistent approach with traditional dynamic traffic assignment based approaches for deployment. The results show the importance of incorporating driver behavior realistically in the determination of the information strategies. Significant differences in terms of system travel time savings and compliance to the information strategies can be obtained when the behavior-consistent approach is compared to the traditional approaches. The behavior-consistent approach can provide more robust performance compared to the standard user or system optimal information strategies. Subject to a meaningful estimation of driver behavior, it can ensure system performance improvement. By contrast, approaches that do not seek to simultaneously achieve the objectives of the drivers and the controller can potentially deteriorate system performance because the controller may over-recommend or under-recommend some routes, or recommend routes that are not considered by the drivers.     

Isolated intersection control for various levels of vehicle technology: Conventional,connected, and automated vehicles

Connected vehicle technology can be beneficial for traffic operations at intersections. The information provided by cars equipped with this technology can be used to design a more efficient signal control strategy. Moreover, it can be possible to control the trajectory of automated vehicles with a centralized controller. This paper builds on a previous signal control algorithm developed for connected vehicles in a simple, single intersection. It improves the previous work by (1) integrating three different stages of technology development; (2) developing a heuristics to switch the signal controls depending on the stage of technology; (3) increasing the computational efficiency with a branch and bound solution method; (4) incorporating trajectory design for automated vehicles; (5) using a Kalman filter to reduce the impact of measurement errors on the final solution. Three categories of vehicles are considered in this paper to represent different stages of this technology: conventional vehicles, connected but non-automated vehicles (connected vehicles), and automated vehicles. The proposed algorithm finds the optimal departure sequence to minimize the total delay based on position information. Within each departure sequence, the algorithm finds the optimal trajectory of automated vehicles that reduces total delay. The optimal departure sequence and trajectories are obtained by a branch and bound method, which shows the potential of generalizing this algorithm to a complex intersection.Simulations are conducted for different total flows, demand ratios and penetration rates of each technology stage (i.e. proportion of each category of vehicles). This algorithm is compared to an actuated signal control algorithm to evaluate its performance. The simulation results show an evident decrease in the total number of stops and delay when using the connected vehicle algorithm for the tested scenarios with information level of as low as 50%. Robustness of this algorithm to different input parameters and measurement noises are also evaluated. Results show that the algorithm is more sensitive to the arrival pattern in high flow scenarios. Results also show that the algorithm works well with the measurement noises. Finally, the results are used to develop a heuristic to switch between the different control algorithms, according to the total demand and penetration rate of each technology.     

An Adaptive Neuro-Fuzzy Inference System for estimating the number of vehicles for queue management at signalized intersections

Queue management is a valuable but underutilized technique which could be used to minimize the negative impacts of queues during oversaturated traffic conditions. One of the main obstacles of applying queue management techniques along signalized arterials is the unavailability of a robust and sufficiently accurate method for measuring the number of vehicles approaching a signalized intersection. The method based on counting vehicles as they enter and exit a specific detection zone with check-in and check-out detectors is unreliable because of the likely systematic under or over counting and the resulting cumulative errors. This paper describes the application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) in the development of a new fuzzy logic-based approach for estimating the Number of Vehicles in a Detection Zone (NVDZ) by using detector time-occupancy data (instead of detector counts). Microscopic simulation results are used to evaluate the accuracy of the NVDZ estimates. Tests were carried out to determine the transferability of a tuned Fuzzy Inference System (FIS) and to check the sensitivity of the calibrated FIS to detection coverage, the location of the detection zone relative to the signalized (bottleneck) intersection, the length of the detection zone, and different signal timings at the bottleneck intersection. Results show that the NVDZ estimation based on fuzzy logic seems to be a feasible approach. Although the primary objective of developing the NVDZ estimation technique has been queue management, other applications such as ramp metering and incident detection could potentially use the same technique.     

New approach for developing warrants of protected left-turn phase at signalized intersections

The research embodied in this paper presents a new approach for the development of guidelines for the installation of a protected left-turn phase at signalized intersections when permissive-only left-turn operation is present. This approach is based on maintaining intersection traffic operation at optimum efficiency. Three analyses were presented and discussed and they involved the use of the new approach on some hypothetical basic scenarios at a four-legged intersection with single lane in each approach. The first scenario involved exclusive left-turn lane operation while the other two scenarios involved shared-lane operation. Exhaustive signal optimization analyses were conducted using a signal optimization software package called “Signal Expert”. Regression models were developed from optimization results that allow the analyst to make the decision on protected left-turn phase installation using the basic input data of signal timing design without the need to perform field measurements. The regression results showed that the transition from permissive to protected/permissive left-turn operation, based on system optimization, is mainly a function of traffic conditions and that this transition (interface) is predictable. The results also suggested that these warrants are of reasonable accuracy when compared with those in the current practice.     

Average duration and performance of actuated signal phases

This paper describes an approach for evaluating alternative traffic detection designs for a signalized intersection. The models described in this paper can be used to determine the average phase duration and frequency of phase “max-out” as a function of the detector loop layout, detector unit timing, traffic demand, and approach speed. Layout and timing are described by the number of detectors on each approach served by the phase, detector location on each approach, detector length, and detector unit and controller time settings. The authors have used the concept of maximum allowable headway (MAH) to combine the many possible combinations of layout and timing variables into one representative quantity, which greatly simplifies the modelling process. The performance models were used to examine the sensitivity of intersection performance to a range of design values. In general, both phase duration and cycle length increase with higher demands or larger MAHs. Multiloop (i.e. two or more detection zones per lane) detector designs typically have larger MAHs than designs with one detector loop per lane. Phase duration and cycle length also increase for very low demand levels. In terms of performance, the maximum green duration was found to have a contrary effect at higher flow conditions. Larger maximum greens were found to reduce delays to the phase in service by reducing the probability of max-out but they increased delays to drivers waiting for service.     

Integrated optimization of bus priority operations in connected vehicle environment

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

Real-time 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.     

Investigating the applicability of exclusive pedestrian phase at two‐phase actuated controlled intersections

The exclusive pedestrian phase (EPP) has been used in many countries to promote walking around downtown areas by increasing the ease and convenience of pedestrian crossing. However, its applicability has not been systematically demonstrated, especially when an intersection is operated in actuated mode. This paper presents an extensive simulation‐based analysis of the applicability of EPP as compared with a normal concurrent pedestrian‐phase pattern at an isolated intersection controlled by actuated logic. Actuated signal control logics for EPP‐actuated and conventional concurrent pedestrian phase‐actuated controls are developed. Both of these control logics consider pedestrian crossing demands and can adapt to changes in vehicle traffic to reduce vehicle delay as well. A simulation model of a two‐phase controlled intersection is built and calibrated based on field data using VISSIM (PTV Planung Transport Verkehr AG in Karlsruhe, Germany). Extensive analysis is conducted to reveal fully the applicable EPP domain in terms of vehicle traffic demand, pedestrian demand, vehicle turning ratio, and pedestrian diagonal crossing ratio. The results show that the performance and applicable domain of EPP are jointly determined by those five factors. EPP significantly outperforms concurrent pedestrian phase if the vehicle turning ratio is greater than 0.6 and the pedestrian diagonal crossing ratio is greater than 0.6. These results can help traffic engineers in choosing the appropriate pedestrian‐phase patterns at actuated signalized intersections. Copyright © 2015 John Wiley & Sons, Ltd.     

Model-based predictive control for bicycling in urban intersections

In this paper, a model predictive control approach for improving the efficiency of bicycling as part of intermodal transportation systems is proposed. Considering a dedicated bicycle lanes infrastructure, the focus in this paper is to optimize the dynamic interaction between bicycles and vehicles at the multimodal urban traffic intersections. In the proposed approach, a dynamic model for the flows, queues, and number of both vehicles and bicycles is explicitly incorporated in the controller. For obtaining a good trade-off between the total time spent by the cyclists and by the drivers, a Pareto analysis is proposed to adjust the objective function of the MPC controller. Simulation results for a two-intersections urban traffic network are presented and the controller is analyzed considering different methods of including in the MPC controller the inflow demands of both vehicles and bicycles.     

Vehicle actuated signal performance under general traffic at an isolated intersection

We study green extension of a two-phased vehicle actuated signal at an isolated intersection between two one-way streets. The green phase is extended by a preset time interval, referred to as critical gap, from the time of a vehicle actuation at an advance detector. The green phase switches if there is no arrival during the critical gap. We develop an exact model to study the intersection performance with traffic following Poisson processes. We further extend the model to approximate the case of general traffic. Our model in the general case works well compared with Monte Carlo simulation. A few major observations include: (1) The optimal critical gap decreases with the traffic; (2) The optimal critical gap can be much larger (up to 5 s) than the common presumption of 2–3 s; (3) Queue clearance policy is not nearly optimal in general even in the case of heavy traffic.     

Spatiotemporal intersection control in a connected and automated vehicle environment

Current research on traffic control has focused on the optimization of either traffic signals or vehicle trajectories. With the rapid development of connected and automated vehicle (CAV) technologies, vehicles equipped with dedicated short-range communications (DSRC) can communicate not only with other CAVs but also with infrastructure. Joint control of vehicle trajectories and traffic signals becomes feasible and may achieve greater benefits regarding system efficiency and environmental sustainability. Traffic control framework is expected to be extended from one dimension (either spatial or temporal) to two dimensions (spatiotemporal). This paper investigates a joint control framework for isolated intersections. The control framework is modeled as a two-stage optimization problem with signal optimization at the first stage and vehicle trajectory control at the second stage. The signal optimization is modeled as a dynamic programming (DP) problem with the objective to minimize vehicle delay. Optimal control theory is applied to the vehicle trajectory control problem with the objective to minimize fuel consumption and emissions. A simplified objective function is adopted to get analytical solutions to the optimal control problem so that the two-stage model is solved efficiently. Simulation results show that the proposed joint control framework is able to reduce both vehicle delay and emissions under a variety of demand levels compared to fixed-time and adaptive signal control when vehicle trajectories are not optimized. The reduced vehicle delay and CO₂ emissions can be as much as 24.0% and 13.8%, respectively for a simple two-phase intersection. Sensitivity analysis suggests that maximum acceleration and deceleration rates have a significant impact on the performance regarding both vehicle delay and emission reduction. Further extension to a full eight-phase intersection shows a similar pattern of delay and emission reduction by the joint control framework.     

A model‐based demand‐balancing control for dynamically divided multiple urban subnetworks

Traffic control is an effective and efficient method for the problem of traffic congestion. It is necessary to design a high‐level controller to regulate the network traffic demands, because traffic congestion is not only caused by the improper management of the traffic network but also to a great extent caused by excessive network traffic demands. Therefore, we design a demand‐balance model predictive controller based on the macroscopic fundamental diagram‐based multi‐subnetwork model, which can optimize the network traffic mobility and the network traffic throughput by regulating the input traffic flows of the subnetworks. Because the transferring traffic flows among subnetworks are indirectly controlled and coordinated by the demand‐balance model predictive controller, the subnetwork division can variate dynamically according to real traffic states, and a global optimality can be achieved for the entire traffic network. The simulation results show the effectiveness of the proposed controller in improving the network traffic throughput. Copyright © 2016 John Wiley & Sons, Ltd.