Speed Advisory Research Articles

A System Optimal Speed Advisory Framework for a Network of Connected and Autonomous Vehicles

The technological advancements involving information and communication technologies (ICT), such as Connected and Automated Vehicles (CAVs) and the Intelligent Transport Systems (ITS), have enabled new efficient traffic control and management strategies to mitigate traffic congestion. Specifically, the combined traffic flow-speed advisory systems based on CAVs and ITS technologies could provide the individual vehicle with the optimal speed to reduce the fuel consumption, the number of stops, simultaneously reduce the network-wide traffic congestion and improve road safety. This article develops a novel bi-level control framework underpinned by the mutual interaction between a system optimal traffic flow control strategy at a network level and a speed control policy for an individual vehicle at a link level within a connected traffic environment. Our framework proposes the novel group-based method to guarantee the consistency and interaction between the macroscopic and microscopic models. To this end, it efficiently optimizes vehicular trajectories while meeting the network-wide objectives which have not been investigated previously in the literature. We propose an efficient algorithm for this problem that iteratively solves mixed-integer linear programming (MILP) models for each upper and lower level. Numerical results indicate the effectiveness of the proposed speed advisory method in vehicular emission reduction, favorable network queue formation, and its positive influence on traffic flow patterns over the network.

Operational effects of speed breakers: a case study in India

Traffic condition in India is highly heterogeneous consisting of vehicles of different static and dynamic characteristics. The speeds of all vehicles are non-uniform. Therefore, to regulate the speeds of different category of vehicles while travelling on a sharp turn or while approaching towards an intersection or buildings like schools and hospitals, traffic calming devices are provided on the roads. Speed breakers are one of the most commonly used traffic calming devices in developing countries like India. Geometry of the speed breakers are based on IRC 99 which says that the width of speed breakers should be 3.7 m and the height should be 0.1 m for urban roads. The codal provisions also suggest that the vehicles should be able to cross the speed breaker at an advisory speed of 20 kmph. However, it is observed that most of the speed breakers have faulty geometrical dimensions owing to various problems to the road users. The width of the speed breakers are usually observed to be narrowed down which increases the chance of road crashes due to sudden braking of vehicles encountering it. Further, it also leads to sudden slowing down of the whole traffic stream leading to traffic congestion. It also reduces fuel efficiency of the vehicles leading to overall increase in vehicle operating and maintenance costs. The present study reviews in detail the provisions provided in the IRC code for construction of speed breakers. Thereafter, a practical on field study is conducted to examine the dimensions of various speed breakers across various cities in India, and compare their dimensions with the provisions provided in the IRC code. Further, the operating speed of the vehicles while crossing the speed breaker is also noted down and compared with the suggested speed as provided in the code. Finally, a detailed comparison between speed reduction at the speed breaker and their dimensions has been analyzed. Speed reduction of different category of vehicles has also been examined in detail.

MPC-CSAS: Multi-Party Computation for Real-Time Privacy-Preserving Speed Advisory Systems

As a part of Advanced Driver Assistance Systems (ADASs), Consensus-based Speed Advisory Systems (CSAS) have been proposed to recommend a common speed to a group of vehicles for specific application purposes, such as emission control and energy management. With Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I) technologies and advanced control theories in place, state-of-the-art CSAS can be designed to get an optimal speed in a privacy-preserving and decentralized manner. However, the current method only works for specific cost functions of vehicles, and its execution usually involves many algorithm iterations leading long convergence time. Therefore, the state-of-the-art design method is not applicable to a CSAS design which requires real-time decision making. In this article, we address the problem by introducing MPC-CSAS, a Multi-Party Computation (MPC) based design approach for privacy-preserving CSAS. Our proposed method is simple to implement and applicable to all types of cost functions of vehicles. Moreover, our simulation results show that the proposed MPC-CSAS can achieve very promising system performance in just one algorithm iteration without using extra infrastructure for a typical CSAS.

Enhanced speed advice for connected vehicles in the proximity of signalized intersections

Technological advancements in the field of transportation are gradually enabling cooperative, connected and automated mobility (CCAM). The progress in information and communication technology (ICT) has provided mature solutions for infrastructure-to-vehicle (I2V) communication, which enables the deployment of Cooperative-ITS (C-ITS) services that can foster comfortable, safe, environmentally friendly, and more efficient traffic operations. This study focuses on the enhancement of speed advice comfort and safety in the proximity of signalized intersections, while ensuring energy and traffic efficiency. A detailed microscopic simulation model of an urban network in the city of Thessaloniki, Greece is used as test bed. The performance of dynamic eco-driving is evaluated for different penetration rates of the dynamic eco-driving technology and varying traffic conditions. The simulation analysis indicates that speed advice can be comfortable and safe without adversely impacting energy and traffic efficiency. However, efficient deployment of dynamic eco-driving depends on road design characteristics, activation distance of the service, traffic signal plans, and prevailing traffic conditions.

Implementation of a Long-Range Air Traffic Flow Management for the Asia-Pacific Region

We provide a concept of operations and a corresponding implementation of a long-range air traffic flow management in the Asia-Pacific region. This management will provide an appropriate demand-capacity balancing considering both aircraft sequencing by local arrival management procedures and flow optimization to prevent over-demand in the approach area around the airport. Thus, coordination of long-range international flights demands collaboration between different flight information regions and local regulations. As Singapore Changi Airport is a central element of the Asia-Pacific flow management high share of long-haul air traffic, we use this airport to demonstrate our approach. To derive the operational conditions and actual traffic patterns at the airport, ADS-B messages and flight plan information are processed. The data are cleaned, analyzed, and filtered to provide information about arrival flows within given distances to the airport. We provide an efficacy analysis of the long-range air traffic flow management using two approaches. First, we applied a mixed-integer optimization of time shifts of normal distributed flight times. Here, the regulation of long-range flights by time shifts (e.g., achieved by speed advisories) shows a significant relief from periods of over-demand at the airport approach sector. Second, we implement a reference and a test case scenario in an agent-based simulation environment including the local arrival management procedures. Here, the number of holdings and the associated holding time could be reduced by at least 26%.

Research on the fuel consumption conservation potential of ADAS on passenger cars

Under the trend of automobile electrification, network connection, and intelligence, EU and USA have carried out fuel-saving research and initiatives on ADAS and CAV. The eCoMove project has aimed at economically optimal driving control and traffic management; MAVEN discusses the technical path of GLOSA (Green Light Optimal Speed Advisory) and ecological auto-driving EAD (Eco-Autonomous Driving) by smoothing the vehicle speed. The American NEXTCAR project contains multiple projects. When supplemented with DSF (Dynamic Skip Fire) and 48V technology, the road test led by Ohio State University resulted in a 15% fuel saving rate. Platoon and optimizing intersection signal lights can offer vehicles a more fuel-efficient condition; slope energy utilization, HEV SOC active management, cold storage evaporator, coasting, 48V and mDSF (miller cycle Dynamic Skip Fire) fuel-saving potential has been fully utilized.

Vehicle-Based Trajectory Specification in Presence of Traffic Lights with Stochastic Switching Times

Vehicle-based GLOSA (Green Light Optimal Speed Advisory) systems use information about the next switching time of the traffic lights to calculate fuel-efficient position and velocity profiles for connected vehicles, according to their current state (position and speed). A stochastic optimal control problem was recently proposed to address the GLOSA problem in cases where the next switching time is decided in real time and is therefore uncertain in advance. The corresponding numerical solution via SDP (Stochastic Dynamic Programming) calls for substantial computational time (few minutes), which excludes problem solution in the vehicle’s computer in real time. This work considers the same stochastic problem of optimal trajectory specification for vehicles approaching a signalized junction with traffic signals operated in real-time (adaptive) mode, due to which the next switching time is stochastic. However, a modified version of Dynamic Programming, known as Discrete Differential Dynamic Programming (DDDP), is used for numerical solution of the stochastic optimal control problem. It is demonstrated, based on a realistic example, that the DDDP algorithm achieves results equivalent to those obtained with the ordinary SDP algorithm, albeit with significantly better performance in terms of computational time. Specifically, the solution is typically obtained in around 1 CPUs, which is real-time feasible and would allow for the DDDP calculations to be executed in the vehicle’s on-board computer.

Driver Behavior Modeling Using Game Engine and Real Vehicle: A Learning-Based Approach

As a good example of Advanced Driver-Assistance Systems (ADAS), Advisory Speed Assistance (ASA) helps improve driving safety and possibly energy efficiency by showing advisory speed to the driver of an intelligent vehicle. However, driver-based speed tracking errors often emerge, due to the perception and reaction delay, as well as imperfect vehicle control, degrading the effectiveness of ASA system. In this study, we propose a learning-based approach to modeling driver behavior, aiming to predict and compensate for the speed tracking errors in real time. Subject drivers are first classified into different types according to their driving behaviors using the k-nearest neighbors (k-NN) algorithm. A nonlinear autoregressive (NAR) neural network is then adopted to predict the speed tracking errors generated by each driver. A specific traffic scenario has been created in a Unity game engine-based driving simulator platform, where ASA system provides advisory driving speed to the driver via a head-up display (HUD). A human-in-the-loop simulation study is conducted by 17 volunteer drivers, revealing a 53% reduction in the speed error variance and a 3% reduction in the energy consumption with the compensation of the speed tracking errors. The results are further validated by a field implementation with a real passenger vehicle.

Evaluating Suitable Glosa-Algorithms by Simulation Considering Realistic Traffic Conditions and V2X-Communication

Abstract Vehicle-to-Everything (V2X) communication allows infrastructure elements, e.g., traffic lights, to directly communicate with vehicles, thereby allowing services like Green Light Optimized Speed Advisory (GLOSA), Probe Vehicle Data (PVD), and Traffic Signal Priority Request (TSP). The idea behind GLOSA is to assist vehicles approaching an intersection with speed advices in order to fulfill a given objective, e.g., minimizing fuel usage, emissions and/or delay. In a prior work (Kloeppel et al., 2019), several GLOSA algorithms were examined and their fitness (in the form of CO2 emissions, fuel usage and delay) were evaluated under largely realistic conditions. This paper is an extension of the prior work and presents further examinations, which include a more detailed study on the behaviour of Diesel-powered vehicles when using the GLOSA algorithm of Stebbins et al. (2017) as well as a study considering fixed-time control coupled with intelligent vehicles.

Evaluation of Alternative Messages and Sign Locations on Driver Response to a Dynamic Speed Feedback Sign on a Freeway Interchange Ramp

Research was undertaken to determine effective messaging strategies and sign positions for dynamic speed feedback signs (DSFS) when used for speed management at freeway ramp curves. A field evaluation was performed in this setting to assess the impacts of a DSFS on driver speed selection and braking characteristics while approaching and entering the curve. Three feedback messaging strategies were evaluated at three sign positions in advance of the curve. Compared with the existing site (without the DSFS), the DSFS reduced curve entry speeds and improved brake response across all test conditions, particularly for heavy trucks. Overall, considering the combination of both sign position and feedback messaging strategy, the greatest benefits to driver behavior were attained when the DSFS was positioned 255 ft upstream of the curve and the feedback message included the speed number alternating with a SLOW DOWN message. The inclusion of an advisory speed panel with the DSFS did not have a substantive impact on driver behavior. Based on the findings, the continued use of DSFS as a speed reduction treatment at freeway ramp curves is recommended. Specifically, the sign should be positioned to provide adequate time for drivers to perceive and react to the message, such that comfortable braking can be accommodated while approaching the curve. However, the sign should not be placed too far in advance of the curve, as drivers may be more likely to disregard such a premature warning message. Further evaluation of DSFS under various alternative ramp configurations is recommended.

Guidance-oriented advanced curve speed warning system in a connected vehicle environment

Connected Vehicles (CV) technology has been used to address safety issues on highway horizontal curves. Existing curve warning systems are either using curve warning signs or providing drivers with an in-vehicle curve warning message in advance, allowing drivers to adjust their speed prior to the vehicle entering the curve. In practice, drivers might be compliant before entering the curve but may pick up the speed in the curve. Therefore, it remains a problem that existing curve warning systems are not able to guide drivers by providing necessary speed warnings through the entire course of approaching, entering, navigating, and leaving horizontal curves. Therefore, the objective of this study is to improve curve speed compliance by proposing a guidance-oriented Advanced Curve Speed Warning system (Advanced-CSW) with a focus on providing guided curve speed messages throughout the horizontal curves. The Advanced-CSW system is based on Dedicated Short-Range Communication (DSRC) enabling vehicle-infrastructure (V2I) communication. Anytime the vehicle is speeding, the guided message will be displayed until the vehicle’s speed is within compliant range. Drivers who use the Advanced-CSW can receive multiple guided messages from the in-vehicle heads-up display through the entire course of navigating through horizontal curves. Thirty participants are recruited to perform the driving experiment on the simulator of driving through a series of horizontal curves under various geometric, roadway and traffic conditions. These conditions include different curve severity, illumination, and pavement wetness levels. The Advanced-CSW system’s performance was evaluated in terms of the speed difference, which measures the gap between the in-curve mean speed and curve advisory speed. The results were compared with the performance of speed difference by driving with CSW or CSO through the entire curve. The experiment data was modeled using the mixed linear model with random effects, which includes the individual’s driving behavior. In summary, when male drivers navigate through the horizontal curves under different curve speed warning systems, their speed compliance is significantly increased with continuous and guided messages provided in comparison with the speed compliance under the one-time curve speed warning message and the curve sign only. Female drivers improve their speed compliance in the curve by using curve signs only comparing to using one-time curve speed warning message or continuous guided curve speed warning messages. Also, male drivers’ speed differences by using the guided system are significantly reduced by 6.53∼7.68 mi/h compared to driving with curve signs only or one-time curve speed warning message. In addition, there is also a speed reduction of 1.81 mi/h if male drivers receiving continuous guided messages in the curve during the daytime than during the nighttime. The proposed adaptive system based on that is adaptive to the vehicle’s real-time speed and location by providing a new direction in designing effective curve warning systems. The speed-guided messages through the entire course of approaching, entering, navigating, and leaving horizontal curves can solve the current issue of speed incompliance by using the existing curve warning systems.

Eco-Driving Algorithm with a Moving Bottleneck on a Single-Lane Road

Eco-driving strategies have been applied to smooth traffic flow and reduce greenhouse gas emissions along with air pollution. In this paper, we propose an eco-driving strategy to reduce traffic oscillation and smooth trajectories for connected vehicles following a moving bottleneck on a single-lane road. The eco-driving strategy, which leverages vehicle-to-vehicle (V2V) communications, designs advisory speed limits for each following vehicle through a control algorithm. The algorithm is based on the prediction of the following vehicle trajectories dictated by a moving bottleneck. The following vehicle trajectories are obtained by analytically solving the moving bottleneck problem in which the moving bottleneck speeds vary over time. In addition, the bounded acceleration rate is imposed in car-following behavior. The benefits of this strategy are demonstrated by applying it to four scenarios with different bottleneck movements. By simulating the scenarios with Newell’s car-following model with bounded acceleration and VT-Micro emission model, we find that both speed fluctuations and emissions are reduced with the algorithm in the scenarios in which the moving bottleneck has a constant speed, accelerates, decelerates and stops-and-goes. The results indicate that the proposed eco-driving algorithm can smooth traffic flow behind a moving bottleneck.

Optimized Speed Trajectories for Cyclists, Based on Personal Preferences and Traffic Light Information-A Stochastic Dynamic Programming Approach

The literature on green mobility and eco-driving in urban areas has burgeoned in recent years, with special attention to using infrastructure to vehicle (I2V) communications to obtain optimal speed trajectory which minimize the economic and environmental costs. This article shares the concept with these studies but turns the spotlight on cyclists. It examines the problem of finding optimal speed trajectory for a cyclist in signalised urban areas. Unlike the available studies on motorised vehicles which predominantly designed for pre-defined, fixed traffic lights timing, this article uses an algorithm based on stochastic dynamic programming to explicitly address uncertainty in traffic light timing. Moreover, through a comprehensive set of simulation experiments, the article examines the impact of the speed advice’s starting point as well as the cyclist’s willingness for changing his/her speed on enhancing the performance. The proposed approach targets various performance metrics such as minimising the total travel time, energy consumption, or the probability of stopping at a red light. Hence, the resulting speed advice can be tailored according to the personal preferences of each cyclist. In a simulation case study, the results of the proposed approach is also compared with an existing approach in the literature.

An improved CAL3QHC model and the application in vehicle emission mitigation schemes for urban signalized intersections

As an important component of urban roads, signalized intersections have contributed significantly to motor vehicle emissions, which however has not been modelled in high accuracy. This paper uses the International Vehicle Emission (IVE) model to obtain the localized emission factors, so that a dispersion model, CAL3QHC was introduced and modified to incorporate the vehicle acceleration and deceleration states. Two emission reduction schemes for urban signalized intersections, the Green Light Optimal Speed Advisory (GLOSA) system and the no-obstacle driveway design, were proposed, by taking the intersection of Wen'er West Road and Liangmu Road in Hangzhou, China as an empirical example. The overall performance and roadside emission dispersion were simulated using VISSIM software package, with the improved CAL3QHC model. The GLOSA system and the no-obstacle driveway design were found to mitigate the roadside CO concentration by 21.2% and 22.1%, respectively. Findings of the study may assist to monitor roadside pollutant concentrations, formulate and evaluate emission mitigation strategies in urban intersections.

An efficient path planning GLOSA‐based approach over large scale and realistic traffic scenario

Vehicle speed and trajectories play an important role in influencing fuel consumption, gas emissions and travel time. ITS services are often proposed in order to reduce the impact of these issues on the environment and the comfort of drivers. Cooperative Intelligent Transport Systems (C‐ITS) introduced many services that target traffic efficiency management and road safety. Green Light Optimal Speed Advisory (GLOSA) is one of the traffic efficiency services that can significantly cut fuel consumption and decrease waiting time while crossing intersections. When approaching a signalized intersection, GLOSA informs the driver of an advisory speed to respect in order to reach the intersection while the traffic light is green. In this paper, we propose an Efficient Path Planning GLOSA‐based (EPP‐GLOSA) approach using Dijkstra greedy algorithm and GLOSA service. EPP‐GLOSA aims at proposing the fastest path for the driver to follow in order to reach its destination. The estimated duration to reach the destination takes into consideration the state of traffic lights along the path on top of Dijkstra algorithm. The proposed approach is evaluated under a large scale scenario in free flow and rush‐hour conditions. Our findings show that EPP‐GLOSA offers a balanced scale between travel time reduction and fuel saving especially in free flow conditions.

Vehicle Trajectory Specification in Presence of Traffic Lights with Known or Uncertain Switching Times

The main purpose of this work is to generate optimal trajectories for vehicles crossing a signalized junction, with traffic signals operated in either fixed-time or real-time (adaptive) mode. In the latter case, the next switching time is decided in real time based on the prevailing traffic conditions and is therefore uncertain in advance. The GLOSA (Green Light Optimal Speed Advisory) problem is addressed by using traffic lights information and calculating a trajectory and velocity profile for the vehicle based on the vehicle’s initial state (position and speed) and a fixed final destination state. At first, an appropriate optimal control problem is formulated and solved analytically via Pontryagin’s minimum principle (PMP) for the case of known switching times. Subsequently, for the case of real-time signals, availability of a time-window of possible signal switching times, along with the corresponding probability distribution, is assumed, and the problem is cast in the format of a stochastic optimal control problem and is solved numerically using stochastic dynamic programming (SDP) techniques. Application results, for various driving scenarios, of the deterministic approach, which considers the case of known switching times, and a comprehensive comparison of the stochastic GLOSA approach with a sub-optimal approach are presented. In particular, it is demonstrated that the proposed SDP approach achieves better average performance compared with the sub-optimal approach because of the better (probabilistic) information on the traffic light switching time.

The impact of the connected environment on driving behavior and safety: A driving simulator study

The connected environment provides surrounding traffic information to drivers via different driving aids that are expected to improve driving behavior and assist in avoiding safety-critical events. These driving aids include speed advisory, car-following assistance, lane-changing support, and advanced information about possible unseen hazards, among many others. While various studies have attempted to examine the effectiveness of different driving aids discretely, it is still vague how drivers perform when they are exposed to a connected environment with vehicle-to-vehicle and vehicle-to-infrastructure communication capabilities. As such, the objective of this study is to examine the effects of the connected environment on driving behavior and safety. To achieve this aim, an innovative driving simulator experiment was designed to mimic a connected environment using the CARRS-Q Advanced Driving Simulator. Two types of driving aids were disseminated in the connected environment: continuous and event-based information. Seventy-eight participants with diverse backgrounds drove the simulator in four driving conditions: baseline (without driving aids), perfect communication (uninterrupted supply of driving aids), communication delay (driving aids are delayed), and communication loss (intermittent loss of driving aids). Various key driving behavior indicators were analyzed and compared across various routine driving tasks such as car-following, lane-changing, interactions with traffic lights, and giving way to pedestrians at pedestrian crossings. Results suggest that drivers in the perfect communication scenario maintain a longer time-to-collision during car-following, a longer time-to-collision to pedestrian, a lower deceleration to avoid a crash during lane-changing, and a lower propensity of yellow light running. Overall, drivers in the connected environment are found to make informed (thus better) decisions towards safe driving.

Safety Evaluation of Green Light Optimal Speed Advisory (GLOSA) System in Real-World Signalized Intersection

The use of green light optimal speed advisory (GLOSA) systems is seen as a key application for achieving more environmentally friendly, time-efficient, and safer traffic flows at signalized intersections. In previous papers, the authors have proposed a GLOSA system that informs drivers of the most appropriate position for their vehicle instead of their optimal speed. This paper reports on a performance evaluation of our proposed GLOSA system after application to morning rush-hour traffic flow in a simulation of a real-world signalized intersection. A performance evaluation of this numerical simulation showed that our GLOSA system increased the time headway of vehicles and decreased their deceleration rates in the vicinity of the signalized intersection. In addition, the use of the system moderately increased fuel efficiency without affecting vehicle travel time. From these results, it can be concluded that our proposed GLOSA system has the potential to create safer traffic flows in real-world signalized intersections without degrading traffic efficiency.

Minimization of Fuel Consumption for Vehicle Trajectories

Eco-driving, a timely and well-known subject, aims at reducing fuel consumption by appropriately maneuvering a vehicle with a human or automated driver. In this work, the eco-driving problem is cast in an optimal control framework. State equations reflect the simple vehicle kinematics for position and speed, with the acceleration acting as a control input. Initial and final states (position and speed) are fixed. For the fuel consumption estimation, a number of alternatives are employed. To start with, a realistic, but nonlinear and non-smooth formula from the literature is considered. Simple smoothing procedures are then applied to enable the application of powerful numerical algorithms for the efficient solution of the resulting nonlinear optimal control problem. Furthermore, simpler quadratic approximations of the nonlinear formula are also considered, which enable analytical problem solutions. A comprehensive comparison on the basis of various driving scenarios demonstrates that the often utilized, but sometimes strongly questioned, square-of-acceleration term delivers excellent approximations for fuel minimizing trajectories in the present setting. A GLOSA (Green Light Optimal Speed Advisory) approach, based on the analytical solution of an optimal control problem is also presented.

Dynamic Eco-Driving on Signalized Arterial Corridors during the Green Phase for the Connected Vehicles

Inappropriate driving behaviours can result in additional fuel consumption and emissions. Drivers can be informed of the accurate signal phase and timing (SPaT) and distance information of the current intersection and downstream intersections via vehicle-to-everything (V2X) communications. The real-time information has been utilized to assist drivers in taking reasonable manoeuvres and gain lots of benefits on fuel consumption and emissions in some existing studies. In order to cooperatively address the optimization problem on the signalized arterial corridors, this paper presents an eco-driving optimization model considering preceding SPaT and position information. This model can be applied to pass two successive traffic signals cooperatively during green phase. In this study, a multi-stage optimal approach is proposed to minimize the fuel consumption. Field experiments are carried out for comparative analysis between the connected vehicle with speed advisory and the uninformed vehicle without speed advisory. The results indicate that the fuel saving of the connected vehicle guided by the dynamic optimization algorithm shows significant improvement. In addition, the rolling optimization among three signalized intersections is conducted and the results show that a considerable improvement can be obtained compared with the one-by-one optimization.