Controlling road congestion via a low-complexity route reservation approach

This work introduces a novel route reservation architecture to manage road traffic within an urban area. The developed routing architecture decomposes the road infrastructure into slots in the spatial and temporal domains and for every vehicle, it makes the appropriate route reservations to avoid traffic congestion while minimizing the traveling time. Under this architecture, any road segment is admissible to be traversed only during time-slots when the accumulated reservations do not exceed its critical density. A road-side unit keeps track of all reservations which are subsequently used to solve the routing problem for each vehicle. Through this routing mechanism, vehicles can either be delayed at their origin or are routed through longer but non-congested routes such that their traveling time is minimized. In this work, the proposed architecture is presented and the resulting route reservation problem is mathematically formulated. Through a complexity analysis of the routing problem, it is shown that for certain cases, the problem reduces to an NP-complete problem. A heuristic solution to the problem is also proposed and is used to conduct realistic simulations across a particular region of the San Francisco area, demonstrating the promising gains of the proposed solution to alleviate traffic congestion.     

Signalized intersections with variable flow rates: II A model for delay estimation

This second part of our work develops a model for delay estimation at intersections whose traffic signal controls are continuously being updated. Generally, these traffic signals are centrally controlled. The foundation for the delay estimation model is based on a queuing theory model called “Preemptive resume discipline for M/G/1 with two priority levels.” This queuing model assumes that two customers arrive at acertain point by a Poisson arrival process, and that one customer has service priority over the second customer. The analogy for the case of intersection control is that the preferred customers are the red lights and the secondary customers are the vehicles. In order to adapt the model to the realistic behavior of vehicle traffic at continuously adjusted signals, components are derived to modify the model. The simulation results of the first part of this work are used to calculate adjustment factors that fairly accurately reproduce the simulated delays. This gives rise to the advantage of using in practice a closed mathematical model, in particular when trying to optimize the operation of signalized intersections at the network level.     

Development of a signal-head-free intersection control logic in a fully connected and autonomous vehicle environment

Establishment of effective cooperation between vehicles and transportation infrastructure improves travel reliability in urban transportation networks. Lack of collaboration, however, exacerbates congestion due mainly to frequent stops at signalized intersections. It is beneficial to develop a control logic that collects basic safety message from approaching connected and autonomous vehicles and guarantees efficient intersection operations with safe and incident free vehicle maneuvers. In this paper, a signal-head-free intersection control logic is formulated into a dynamic programming model that aims to maximize the intersection throughput. A stochastic look-ahead technique is proposed based on Monte Carlo tree search algorithm to determine the near-optimal actions (i.e., acceleration rates) over time to prevent movement conflicts. Our numerical results confirm that the proposed technique can solve the problem efficiently and addresses the consequences of existing traffic signals. The proposed approach, while completely avoids incidents at intersections, significantly reduces travel time (ranging between 59.4% and 83.7% when compared to fixed-time and fully-actuated control strategies) at intersections under various demand patterns.     

Mobility and environment improvement of signalized networks through Vehicle-to-Infrastructure (V2I) communications

Traffic signals, even though crucial for safe operations of busy intersections, are one of the leading causes of travel delays in urban settings, as well as the reason why billions of gallons of fuel are burned, and tons of toxic pollutants released to the atmosphere each year by idling engines. Recent advances in cellular networks and dedicated short-range communications make Vehicle-to-Infrastructure (V2I) communications a reality, as individual cars and traffic signals can now be equipped with communication and computing devices. In this paper, we first presented an integrated simulator with V2I, a car-following model and an emission model to simulate the behavior of vehicles at signalized intersections and calculate travel delays in queues, vehicle emissions, and fuel consumption. We then present a hierarchical green driving strategy based on feedback control to smooth stop-and-go traffic in signalized networks, where signals can disseminate traffic signal information and loop detector data to connected vehicles through V2I communications. In this strategy, the control variable is an individual advisory speed limit for each equipped vehicle, which is calculated from its location, signal settings, and traffic conditions. Finally, we quantify the mobility and environment improvements of the green driving strategy with respect to market penetration rates of equipped vehicles, traffic conditions, communication characteristics, location accuracy, and the car-following model itself, both in isolated and non-isolated intersections. In particular, we demonstrate savings of around 15% in travel delays and around 8% in fuel consumption and greenhouse gas emissions. Different from many existing ecodriving strategies in signalized road networks, where vehicles’ speed profiles are totally controlled, our strategy is hierarchical, since only the speed limit is provided, and vehicles still have to follow their leaders. Such a strategy is crucial for maintaining safety with mixed vehicles.     

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.     

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.     

The evaluation of active bus-priority signal routes

In an attempt to provide priority facilities for high occupancy vehicles, many cities have investigated or installed active bus priority signals at selected intersections. This paper describes one such installation at the intersection of Bell Street and Oriel Road in Heidelberg, Victoria, Australia. In particular, it describes the impact of the signals on bus performance levels and on non-priority traffic performance levels. An evaluation is performed taking account of the costs of the installation, the changes in the amount of fuel consumed and the changes in the perceived, budgeted delay of people passing through the intersection. Perceived, budgeted delay is defined so as to take account of the variability of delay and the perception of small delay changes. The evaluation at this site showed that, for various reasons, it was difficult to justify the priority signals at this isolated intersection.The evaluation is then extended to the concept of a route of bus priority signal intersections. A simple model is built to simulate the performance of such a priority route and the evaluation repeated. As a result of using perceived delay as a measure of performance, it is then shown that although the priority intersections along the route may not be individually justifiable, the priority route as a whole may show considerable net benefits. These benefits will occur given that there are greater than a critical number of priority intersections on the route. This critical number will depend on the assumptions made in the priority route model.It is concluded that re-evaluation of bus priority signal intersections along the lines suggested in the paper may yield a number of viable bus priority intersections and bus priority routes which were previously considered to be non-viable.     

Continuum signalized junction model for dynamic traffic networks: Offset,spillback, and multiple signal phases

This paper extends the continuum signalized intersection model exhaustively studied in Han et al. (2014) to more accurately account for three realistic complications: signal offsets, queue spillbacks, and complex signal phasing schemes. The model extensions are derived theoretically based on signal cycle, green split, and offset, and are shown to approximate well traffic operations at signalized intersections treated using the traditional (and more realistic) on-and-off model. We propose a generalized continuum signal model, which explicitly handles complex vehicle spillback patterns on signalized networks with provable error estimates. Under mild conditions, the errors are small and bounded by fixed values that do not grow with time. Overall, this represents a significant improvement over the original continuum model, which had errors that grew quickly with time in the presence of any queue spillbacks and for which errors were not explicitly derived for different offset cases. Thus, the new model is able to more accurately approximate traffic dynamics in large networks with multiple signals under more realistic conditions. We also qualitatively describe how this new model can be applied to several realistic intersection configurations that might be encountered in typical urban networks. These include intersections with multiple entry and exit links, complex signal phasing, all-red times, and the presence of dedicated turning lanes. Numerical tests of the models show remarkable consistency with the on-and-off model, as expected from the theory, with the added benefit of significant computational savings and higher signal control resolution when using the continuum model.     

Automated transit headway control via adaptive signal priority

This paper reports on a study that developed a next‐generation Transit Signal Priority (TSP) strategy, Adaptive TSP, that controls adaptively transit operations of high frequency routes using traffic signals, thus automating the operations control task and relieving transit agencies of this burden. The underlying algorithm is based on Reinforcement Learning (RL), an emerging Artificial Intelligence method. The developed RL agent is responsible for determining the best duration of each signal phase such that transit vehicles can recover to the scheduled headway taking into consideration practical phase length constraints. A case study was carried out by employing the microscopic traffic simulation software Paramics to simulate transit and traffic operations at one signalized intersection along the King Streetcar route in downtown Toronto. The results show that the control policy learned by the agent could effectively reduce the transit headway deviation and causes smaller disruption to cross street traffic compared with the existing unconditional transit signal priority algorithm.     

Modeling and controlling an isolated urban intersection based on cooperative vehicles

In this paper, we propose a new approach for controlling the traffic at isolated intersections. We assume that all vehicles are equipped with on-board units (ITS station) that make them able to wirelessly negotiate the “right of way” according to the measurements done by the positioning system during their travel. A vehicle is allowed to cross the intersection if the green color is displayed to the driver in an on-board screen. The control aims to smooth the traffic through the sequence of vehicles authorized to traverse the intersection. The main challenge raised with the assumption is that the sequence must be dynamically formed by a real time application. The dynamic behavior of the traffic is considered discrete, in order to determine the switching rule according to the instantly observed events. We propose a model based on Timed Petri Nets with Multipliers (TPNM) which allows us to propose the control policy through the structural analysis. The resulting switching rules are very simplistic and efficient for isolated intersections. Indeed, microscopic simulations show that they perform as well as the optimal sequence based on the detection of vehicles at the entrance of the intersection. Moreover, the proposed approach has been tested through a real intersection.     

A dynamic evacuation model for pedestrian–vehicle mixed-flow networks

In urban emergency evacuation, a potentially large number of evacuees may depend either on transit or other modes, or need to walk a long distance, to access their passenger cars. In the process of approaching the designated pick-up points or parking areas for evacuation, the massive number of pedestrians may cause tremendous burden to vehicles in the roadway network. Responsible agencies often need to contend with congestion incurred by massive vehicles emanating from parking garages, evacuation buses generated from bus stops, and the conflicts between evacuees and vehicles at intersections. Hence, an effective plan for such evacuation needs to concurrently address both the multi-modal traffic route assignment and the optimization of network signal controls for mixed traffic flows. This paper presents an integrated model to produce the optimal distribution of vehicle and pedestrian flows, and the responsive network signal plan for massive mixed pedestrian–vehicle flows within the evacuation zone. The proposed model features its effectiveness in accounting for multiple types of evacuation vehicles, the interdependent relations between pedestrian and vehicle flows via some conversion locations, and the inevitable conflicts between intersection turning vehicle and pedestrian flows. An illustrating example concerning an evacuation around the M&T stadium area has been presented, and the results indicate the promising properties of our proposed model, especially on reflecting the complex interactions between vehicle and pedestrian flows and the favorable use of high-occupancy vehicles for evacuation operations.     

Using connected vehicle technology to improve the efficiency of intersections

Information from connected vehicles, such as the position and speed of individual vehicles, can be used to optimize traffic operations at an intersection. This paper proposes such an algorithm for two one-way-streets assuming that only a certain percentage of cars are equipped with this technology. The algorithm enumerates different sequences of cars discharging from the intersection to minimize the objective function. Benefits of platooning (multiple cars consecutively discharging from a queue) and signal flexibility (adaptability to demand) are also considered. The goal is to gain insights about the value (in terms of delay savings) of using connected vehicle technology for intersection control.Simulations are conducted for different total demand values and demand ratios to understand the effects of changing the minimum green time at the signal and the penetration rate of connected cars. Using autonomous vehicle control systems, the signal could rapidly change the direction of priority without relying on the reaction of drivers. However, without this technology a minimum green time is necessary. The results of the simulations show that a minimum green time increases the delay only for the low and balanced demand scenarios. Therefore, the value of using cars with autonomous vehicle control can only be seen at intersections with this kind of demand patterns, and could result in up to 7% decrease in delay. On the other hand, using information from connected vehicles to better adapt the traffic signal has proven to be indeed very valuable. Increases in the penetration rate from 0% up to 60% can significantly reduce the average delay (in low demand scenarios a decrease in delay of up to 60% can be observed). That being said, after a penetration rate of 60%, while the delays continue to decrease, the rate of reduction decreases and the marginal value of information from communication technologies diminishes. Overall, it is observed that connected vehicle technology could significantly improve the operation of traffic at signalized intersections, at least under the proposed algorithm.     

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.     

A multiclass cell transmission model for shared human and autonomous vehicle roads

Autonomous vehicles have the potential to improve link and intersection traffic behavior. Computer reaction times may admit reduced following headways and increase capacity and backwards wave speed. The degree of these improvements will depend on the proportion of autonomous vehicles in the network. To model arbitrary shared road scenarios, we develop a multiclass cell transmission model that admits variations in capacity and backwards wave speed in response to class proportions within each cell. The multiclass cell transmission model is shown to be consistent with the hydrodynamic theory. This paper then develops a car following model incorporating driver reaction time to predict capacity and backwards wave speed for multiclass scenarios. For intersection modeling, we adapt the legacy early method for intelligent traffic management (Bento et al., 2013) to general simulation-based dynamic traffic assignment models. Empirical results on a city network show that intersection controls are a major bottleneck in the model, and that the legacy early method improves over traffic signals when the autonomous vehicle proportion is sufficiently high.     

Signalized intersections with variable flow rates: I analysis and simulation

One of the most common measures of signalized intersection operation is the amount of delay a vehicle incurs while passing through the intersection. Traditional models for estimating vehicle delay at intersections generally assume fixed signal timing and uniform arrival rates for vehicles approaching the intersection. One would expect that highly variable arrival rates would result in much longer delays than uniform arrival rates of the same average magnitude. Furthermore, one might expect that signal timing that is adjusted according to traffic volume would result in lower delay signal when variations in flow warrant such adjustable timing. This paper attempts to test several hypotheses concerning the effects of variable traffic arrival rates and adjusted signal timing through the use of simulation. The simulation results corroborate the hypothesis concerning the effect of varying arrival rates. As the variance of the arrival rate over time increases, the average delay per vehicle also increases. Signal timing adjustments based on traffic appear to decrease delay when flow rates vary greatly. As flow variations stabilize, the benefits of signal adjustments tend to diminish.     

Efficient intersection control for minimally guided vehicles: A self-organised and decentralised approach

An important question for the practical applicability of the highly efficient traffic intersection control is about the minimal level of intelligence the vehicles need to have so as to move beyond the traffic light control. We propose an efficient intersection traffic control scheme without the traffic lights, that only requires a majority of vehicles on the road to be equipped with a simple driver assistance system. The algorithm of our scheme is completely decentralised, and takes into full account the non-linear interaction between the vehicles at high density. For vehicles approaching the intersection in different directions, our algorithm imposes simple interactions between vehicles around the intersection, by defining specific conditions on the real-time basis, for which the involved vehicles are required to briefly adjust their dynamics. This leads to a self-organised traffic flow that is safe, robust, and efficient. We also take into account of the driver comfort level and study its effect on the control efficiency. The scheme has low technological barrier, minimal impact on the conventional driving behaviour, and can coexist with the traffic light control. It also has the advantages of being easily scalable, and fully compatible with both the conventional road systems as well as the futuristic scenario in which driverless vehicles dominate the road. The mathematical formulation of our scheme permits large scale realistic numerical simulations of busy intersections, allowing a more complete evaluation of the control performance, instead of just the collision avoidance at the intersection.     

Evaluating effects of traffic and vehicle characteristics on vehicular emissions near traffic intersections

Urban air quality is generally poor at traffic intersections due to variations in vehicles’ speeds as they approach and leave. This paper examines the effect of traffic, vehicle and road characteristics on vehicular emissions with a view to understand a link between emissions and the most likely influencing and measurable characteristics. It demonstrates the relationships of traffic, vehicle and intersection characteristics with vehicular exhaust emissions and reviews the traffic flow and emission models. Most studies have found that vehicular exhaust emissions near traffic intersections are largely dependent on fleet speed, deceleration speed, queuing time in idle mode with a red signal time, acceleration speed, queue length, traffic-flow rate and ambient conditions. The vehicular composition also affects emissions. These parameters can be quantified and incorporated into the emission models. There is no validated methodology to quantify some non-measurable parameters such as driving behaviour, pedestrian activity, and road conditions     

Distributed coordinated signal timing optimization in connected transportation networks

This paper presents a Distributed-Coordinated methodology for signal timing optimization in connected urban street networks. The underlying assumption is that all vehicles and intersections are connected and intersections can share information with each other. The novelty of the work arises from reformulating the signal timing optimization problem from a central architecture, where all signal timing parameters are optimized in one mathematical program, to a decentralized approach, where a mathematical program controls the timing of only a single intersection. As a result of this distribution, the complexity of the problem is significantly reduced thus, the proposed approach is real-time and scalable. Furthermore, distributed mathematical programs continuously coordinate with each other to avoid finding locally optimal solutions and to move towards global optimality. We proposed a real-time and scalable solution technique to solve the problem and applied it to several case study networks under various demand patterns. The algorithm controlled queue length and maximized intersection throughput (between 1% and 5% increase compared to the actuated coordinated signals optimized in VISTRO) and reduced travel time (between 17% and 48% decrease compared to actuated coordinated signals) in all cases.     

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.