Increase Road Capacity Research Articles

ПОДХОД К УПРАВЛЕНИЮ ТРАНСПОРТНЫМИ ПОТОКАМИ НА ОСНОВЕ СТАНДАРТА МЭК 61499

The number of vehicles on public roads is constantly increasing, and the development of roadinfrastructure is proceeding at a slow pace, and not high-quality transport management entails anincrease in transportation costs, an increase in accidents, noise levels, and environmental pollution.As a consequence, there is a need to apply advanced algorithms and approaches to transport managementin order to maximize the use of the existing road network and increase road capacity. In thecourse of recent studies, it has been revealed that adaptive approaches to traffic management aremost effective on sections of the road network with high traffic intensity and variability. The essenceof the approaches to adaptive management used today is that they are based on the analysis of trafficcongestion and change the phases of traffic light operation depending on the received data in realtime .. Adaptive traffic management shows much better results compared to tight control , significantlyreduces transport delays, travel time and emissions of harmful substances into the atmosphere,therefore, modern researchers are developing new and improving existing approaches and algorithmsfor adaptive transport control. For example, traffic management approaches based on theconcept of IoT and the use of cloud computing are actively developing. The concepts of applying theagent-based approach to adaptive control are also being developed. The paper proposes a methodfor managing traffic flows and automating road infrastructure using an agent-based approach. Theproposed approach includes distributed management of various elements of the road network andtheir direct interconnection with each other. To implement this concept, the open standard of distributedcontrol and automation systems IEC 61499 was used, and to test the feasibility of implementation,several models of traffic intersections were used, one of which was created on the basis of realdata and SUMO - a microscopic and continuous traffic simulation package.

Does Building Highways Reduce Traffic Congestion?

In a seminal study, Duranton and Turner (2011) finds evidence that points to the existence of the fundamental law of highway congestion in the US. They build a causal model using an instrumental variable (IV) approach that yields an estimate of 1.03 for the elasticity of vehicle miles traveled (VMT) to the stock of interstate highways in US metropolitan areas. The result means that government efforts to alleviate traffic congestion by expanding capacity are likely to fail — any increase in the stock of highways is accompanied by a commensurate increase in VMT, leaving travel times unaffected. In this article, we explore the impact of unobserved heterogeneity on the fundamental law. We begin by using a simple partial equilibrium model to demonstrate how metropolitan statistical areas (MSAs) that are identical in most respects but have different initial congestion levels respond differently to added capacity due to individual differences. These differences in MSAs gives rise to heterogeneity in the elasticity of VMT to capacity. We derive conditions under which the elasticity decreases with the initial congestion level. We then revisit the empirical analysis in Duranton and Turner (2011) using the instrumental variable quantile regression (IV-QR) model due to Chernozhukov and Hansen (2005, 2006, 2008). The IV-QR model allows us to incorporate variation in the elasticity due to the presence of unobserved differences across MSAs. Moreover, it allows us to evaluate the impact of changes in the stock of interstate highways on the entire conditional distribution of VMT, not just the impact on the conditional mean as in Duranton and Turner (2011). The IV-QR estimates show that as predicted by the simple partial equilibrium model, the elasticity declines as one goes up the quantile ladder, being more than one at the lower quantiles and less than one at the higher quantiles. The median IV-QR estimate being close to one. The IV-QR model implies that among observationally identical cities, expanding road capacity can lower the number of cities experiencing severe congestion, although the mean or median congestion levels are likely to remain constant. We also estimate the impact of increased road capacity on the unconditional distribution of VMT using the generalized quantile regression (GQR) model due to Powell (Forthcoming). The GQR estimates mirror the IV-QR estimates, but their conclusions are starker at the upper quantiles: building highways have no statistically significant impact on VMT at the highest quantiles. The GQR results imply that building roads can lower the total number of cities having different observed characteristics experiencing severe congestion levels. We further explore the mechanisms that drive the empirical findings by running simulations using a spatial general equilibrium model with an extensive road network calibrated to the Greater Los Angeles (LA) Region. In the general equilibrium model, besides commute trips, consumers have to travel to different zones to acquire consumption goods. The model considers route choice in the network and mode choice. Building roads affect road traffic through three channels: the total amount of consumption, mode choice, and the substitutions among goods sold at different locations. We find that the elasticity of VMT to capacity in LA is 0.321, and the elasticity decreases consistently with the initial congestion level. We report the welfare effect and the changes in other travel-related variables. Our results have important policy implications in that they show that while building roads is unlikely to change the mean or median congestion level, it can reduce the number of cities experiencing high congestion levels.

The potential impacts of automated cars on urban transport: An exploratory analysis

ObjectiveThe concept of automated cars is rapidly becoming a reality. Yet there has been very little analysis of the impacts of such developments on the performance of urban transport systems. These impacts are potentially complex. On the positive side, automation has the potential to increase road capacity, make driving available to more people, and reduce accidents and emissions. On the negative side, it could attract users away from public transport, walking and cycling, substantially increase traffic levels and stimulate urban sprawl. These impacts cannot currently be measured empirically and, by the time that they can, it will be too late to change the implementation model to rectify any resulting problems. Predictive assessments are therefore needed. This paper considers the possible impacts of automated vehicles, predicts their effects on the urban land use and transport system, and discusses the policy implications. We focus on automation of the car fleet, and do not consider the potential of automation of public transport or freight. MethodsWe consider the literature on the range of attributes of automated vehicles which might affect transport and land use patterns, and suggest potential outcomes for each over the period to 2050. These include the proportion of automated vehicles in the car fleet, whether automated vehicles are privately purchased or publicly shared, the impacts on network capacity, the reduced need to pay for and walk from parking places, the potential reduction in the value of in-vehicle time and the potential use by current non-drivers. We represent these attributes in an expanded causal link diagram of the urban land use and transport system and import those causal links into the MARS system dynamics model. We determine from the literature a level for each attribute, and test the impacts in a set of ten scenarios using an updated MARS model of Leeds. ResultsBased on our input assumptions, we find that car-km in 2050 could be over 50% higher than in the business as usual scenario. Public transport use could fall by 18%, threatening accessibility for those dependent on it, while walking and cycling could fall by 13%, reducing their health benefits. Overall person-km would rise, suggesting a reduction in sustainability. A requirement that all automated cars are shared vehicles could reduce these adverse impacts somewhat, but the effects are sensitive to the charge per km. DiscussionOur use of a single value for each attribute means that our analysis is exploratory, but the size of the resulting impacts demonstrates the importance of understanding the scale of systems response to each of the attributes which we have considered. It will be important to manage the way in which automated cars are introduced into urban areas, if they are not to lead to a worsening of the urban environment, accessibility and health. A requirement to make all such vehicles part of shared fleets offers one way forward, but more work is needed to understand the way in which use of such fleets should be charged.

Air pollution emission effects of changes in transport supply: the case of Bogotá, Colombia.

Transportation policy and planning decisions, including decisions on new infrastructure and public transport improvements, affect local and global environmental conditions. This work studies the effect of increased road capacity on traffic-related emissions in Bogotá using a tool that couples a transportation model with emission factors from COPERT IV. We followed a parametric approach varying transport supply and demand, comparing three scenarios: a baseline scenario that represents the transportation system in Bogota in 2015; scenario 1 incorporates five highway capacity-enhancement projects in Bogotá and assumes insensitive travel demand; scenario 2 incorporates the new highway projects but assumes a demand increase of 13% in vehicle trips with private cars. Results include daily and annual values of traffic-related emissions of five air pollutant criteria: CO, NOx, PM10, SO2, and VOC for the baselinescenario, scenario 1, and scenario 2. We found a reduction in emissions after adding highway capacity and assuming inelastic demand (scenario 1). Scenario 1 results in a 15% reduction in PM10 emissions and a 10% reduction in NOx emissions. In contrast, results for scenario 2 suggest increased emissions for all air pollutant criteria (e.g., VOC and CO emissions increase by 21% and 22% compared with the baseline scenario). Therefore, new traffic demand would eliminate the emission savings observed in scenario 1 and could potentially further degrade air quality in Bogotá. While an exact estimate of induced demand that may result from highway expansion in Bogotá is not available, this analysis highlights that such projects could lead to an increase in emissions unless there is a combined effort to managing demand of private vehicle trips.

The solution of traffic congestion during flyover construction at Antapani intersection in Bandung

The Construction of Antapani Flyover has caused traffic congestion. This paper is to find the solution of congestions to achieve the queuing standard allowed referring to PKJI 2014. The method of research uses deductive and inductive which are analysed using the risk analysis of traffic disruptions. The scope of analysis is focused on the two critical construction works, namely abutment and bridge wall works. The traffic congestion analysis considers the narrowing road due to construction disruption. The traffic congestion analysis was carried out by comparing the congestion of the existing construction with the construction method proposed. The three proposed alternative solutions are by the increasing road capacity, scheduling construction methods, and a combining solution those two alternatives. The results show that the best alternative solution to reduce traffic congestion during construction is a combined solution between the increasing road capacity and scheduling construction methods. The result of alternative solution shows the decrease of queue on Terusan Jakarta road reaching up to 90% from the existing condition. The conclusion describes that the narrowing road of work zones of construction has a huge influence on traffic congestion. By this result is suggested to consider the magnitude of disturbance as well as the solution of the traffic congestion that may occurred.

Response Time and Time Headway of an Adaptive Cruise Control. An Empirical Characterization and Potential Impacts on Road Capacity

Road vehicles are characterized by increasing levels of automation and it is vital to understand the future impact on transport efficiency. Adaptive Cruise Control (ACC) is one of the first and most common automated functionalities available in privately owned vehicles. The effect of ACC on traffic flow has been widely studied by making assumptions on its operating strategy and on some of its important parameters such as the response time and the desired time headway. In the literature, these parameters are usually set to low values, based on the vehicle controller’s theoretical ability to respond within a very short time frame. Response time is known to be an important parameter in defining the capacity of the road and therefore, assuming a very short response time, studies usually conclude that systems like the ACC will contribute increasing the road capacity significantly. The present study aims at measuring the actual response time of an ACC-enabled vehicle in car-following conditions. A new methodology for the estimation of the controller’s response time and the desired time-gap was developed to this objective. Results show that the response time of the particular ACC controller was in the range 0.8s-1.2s, which is similar to what is commonly assumed for human drivers. In this light, the results of the present study question the common assumption that ACC or other automation technologies necessarily improve traffic flow and increase road capacity.

Train Speed Trajectory Optimization With On-Board Energy Storage Device

Rail transportation is used extensively in urban areas to reduce CO2 emission and increase road capacity. As a result, the energy efficiency of rail transportation is becoming a popular research topic. Energy-efficient train operation involves four types of control: maximal traction, cruising, coasting, and maximal braking. With the rapid development of energy storage devices (ESDs), this paper aims to develop an integrated optimization model to obtain the speed trajectory with the constraint of on-board ESD properties such as capacity, initial state of energy (SOE), and the degradation of the on-board ESD. Mixed integer linear programming is implemented on the discretized distance-based model to find the solution. In addition to the optimal speed trajectory, the on-board ESD discharge/charge operation strategy will be obtained. Results show that with high on-board ESD capacity, a train tends to apply more braking to recover more energy by using ESD; high initial SOE leads to more traction operation but undermines the energy-recovering process; degradation of on-board ESD influences the train operation and net energy consumption by affecting discharge/charge frequency. Additionally, in the case with on-board ESD, more than 11.6% of net energy consumption can be reduced compared with the one without on-board ESD. This paper indicates that the proposed method is an effective and robust way to study how on-board ESD influences optimal train operation as well as to achieve optimal train speed trajectory with on-board ESD.

Effectiveness of Ring Roads in Reducing Traffic Congestion in Cities for Long Run: Big Almaty Ring Road Case Study

It is common to increase road capacity by constructing ring roads to reduce traffic congestion in city areas, although this is often found to be ineffective in the long run. Accordingly, this study investigates various traffic congestion management approaches and their effectiveness in major cities, and explores an identical transport problem in Almaty, Kazakhstan: The Big Almaty Ring Road (BAKAD). Several case examples from the existing literature are examined in which various approaches were taken for managing traffic congestion problems, and these approaches are classified into three concepts. The first concept comprises heavy engineering measures such as ring road development, new road construction, expansion of existing roads, etc. Such measures can initially reduce traffic congestion, but often become ineffective with time due to the generation of induced traffic. Many cities have taken Push and Pull measures that ensure more efficient use of existing capacity and have initiated environmentally friendly alternative transportation modes such as decreased car usage; promotion of public transport, biking and walking; minimization of the necessity of people’s movement by changing urban land use patterns; and so on. These approaches have been found to be effective in providing sustainable transportation solutions and are classified as concept 2. Nevertheless, Push and Pull measures might not be enough for managing traffic congestion, and it might be necessary to increase the road capacity through heavy engineering measures, especially if the city experiences heavy transit traffic. This combined approach is categorized as concept 3. Consequently, the BAKAD project is examined under the umbrella of three concepts, and recommendations are provided based on the findings from the experience of different cities and interviews with experts from Almaty city. Both the results and recommendations developed are relevant for this specific case only, and are not necessarily transferable.

Safety Effects of Freeway Hard Shoulder Running

Hard shoulder running (HSR) has been increasingly used as a sustainable and viable way to increase road capacity. This study investigated the safety effect of HSR on freeways in South Korea using the empirical Bayes method. This study found an increase in the total number of crashes. In terms of crash severity, a higher proportion of crashes (25.3%) on 2(3)-lane sections were found to be serious (involving injuries and/or fatalities) compared to those on 4(5)-lane sections (3.6%). Also, a positive relationship was found between the length of the hard shoulder running and changes in crash frequencies. Thus, hard shoulder running on lengthy 2(3)-lane freeways should be avoided.

Modelling Reliability of Transportation Systems to Reduce Traffic Congestion

Reliability reflects the level of ease of people and goods to travel. Reliability relates with the variability of travel time, speed, and system usage and transportation system capacity. The higher the capacity of the system and the faster the travel time, the higher the level of reliability of the transportation system, so that it can reduce traffic congestion. Traffic congestion will occur when travel requests exceed road capacity. Our research attempts to provides a comprehensive and objective assessment of improving reliability of transportation systems and its impact to reduce traffic congestion. We utilized system dynamics simulation model to test and evaluate the alternatives of future strategies to increase the reliability of transportation systems and its impact to reduce traffic congestion. Systems dynamics models can be developed at the macroscopic and microscopic levels of transportation systems as well as to evaluate the effects of different transportation policies on traffic congestion. Simulation results show that reliability is determined by several factors such as travel time, headway (the time between two means of transportation to pass a point / place), passenger wait time; access time and egress time (the time needed to get off the vehicle when it arrives at the destination). Transportation systems reliability continues to decline so that in 2017, reliability was only around 41.5%. The improvement scenario of transportation system reliability can be done by conducting several strategies such as increasing road capacity, increasing public vehicle routes around public facilities, as well as increasing the supply of public transportation which has an impact on headway and waiting time. By conducting these strategies, the reliability of transportation systems could be increased to be around 53.3% and a gradual increase in road capacity could be done with a growth of 2.8% per year. Within this condition, the traffic congestion is projected to be around 74.9% - 83.6% in the period 2019-2027, and then to be around 83-88% in the period 2028-2035.

Simulation of Road Capacity Considering the Influence of Buses

The traditional cellular automata model cannot fully demonstrate the effect of buses on road capacity due to the incapable of considering the difference in dynamic performance and driving behaviors between buses and other vehicles. In this paper, a novel cellular automata model accounting for car-following and lane-changing behaviors was developed to investigate the relationship between the multilane road capacity and the proportion of buses. The parameters of the model were calibrated and verified using the data collected from a real-word road with heterogeneous traffic flow. The calibration result indicates that the proposed model can accurately describe the evolution of traffic dynamics on multilane roads. It is found that drivers have a specific preference in determining the target lane in the lane-changing process. Under the same conditions, bus drivers prefer to choose the side lane of the road, while drivers of other vehicles prefer to select the middle lanes. Besides, the preference for the target lanes affects the lane-changing behaviors of the drivers, resulting in a considerable difference in optimal traffic density and maximum traffic volume in different lanes. Moreover, the simulation result suggests that the proportion of buses has a significant impact on road capacity (e.g., the capacity is reduced by as much as 18%). To increase road capacity and improve bus services, based on the proposed model, transit authorities can dynamically allocate the limited road space to buses and other vehicles according to the proportion of buses.

CONGESTION EFFECTS OF AUTONOMOUS TAXI FLEETS

Fleets of shared Autonomous Vehicles (AVs) could replace private cars by providing a taxi-like service but at a cost similar to driving a private car. On the one hand, large Autonomous Taxi (AT) fleets may result in increased road capacity and lower demand for parking spaces. On the other hand, an increase in vehicle trips is very likely, as travelling becomes more convenient and affordable, and additionally, ATs need to drive unoccupied between requests. This study evaluates the impact of a city-wide introduction of ATs on traffic congestion. The analysis is based on a multi-agent transport simulation (MATSim) of Berlin (Germany) and the neighbouring Brandenburg area. The central focus is on precise simulation of both real-time AT operation and mixed autonomous/conventional vehicle traffic flow. Different ratios of replacing private car trips with AT trips are used to estimate the possible effects at different stages of introducing such services. The obtained results suggest that large fleets operating in cities may have a positive effect on traffic if road capacity increases according to current predictions. ATs will practically eliminate traffic congestion, even in the city centre, despite the increase in traffic volume. However, given no flow capacity improvement, such services cannot be introduced on a large scale, since the induced additional traffic volume will intensify today’s congestion.

Management of traffic congestion in adaptive traffic signals using a novel classification-based approach

ABSTRACTTraffic congestion is a critical problem which makes roads busy. Traffic congestion challenges traffic flow in urban areas. A growing urban area creates complex traffic problems in daily life. Congestion phenomena cannot be resolved only by applying physical constructs such as building bridges and motorways and increasing road capacity. It is necessary to build technological systems for transportation management to control the traffic phenomenon. In this article, a new idea is proposed to tackle traffic congestion with the aid of machine learning approaches. A new strategy based on a tree-like configuration (i.e. a decision-making model) is suggested to handle traffic congestion at intersections using adaptive traffic signals. Different traffic networks with different sizes, varying from nine to 400 intersections, are examined. Numerical results and discussion are presented to prove the efficiency and application of the proposed strategy to alleviate traffic congestion.

Roads in transition: Integrated modeling of a manufacturer-traveler-infrastructure system in a mixed autonomous/human driving environment

This paper develops an integrated model to characterize the market penetration of autonomous vehicles (AVs) in urban transportation networks. The model explicitly accounts for the interplay among the AV manufacturer, travelers with heterogeneous values of travel time (VOTT), and road infrastructure capacity. By making in-vehicle time use more leisurely or productive, AVs reduce travelers’ VOTT. In addition, AVs can move closer together than human-driven vehicles because of shorter safe reaction time, which leads to increased road capacity. On the other hand, the use of AV technologies means added manufacturing cost and higher price. Thus, traveler adoption of AVs will trade VOTT savings with additional out-of-pocket cost. The model is structured as a leader (AV manufacturer)-follower (traveler) game. Given the cost of producing AVs, the AV manufacturer sets AV price to maximize profit while anticipating AV market penetration. Given an AV price, the vehicle and routing choice of heterogeneous travelers are modeled by combining a multinomial logit model with multi-modal multi-class user equilibrium (UE). The overall problem is formulated as a mathematical program with complementarity constraints (MPCC), which is challenging to solve. We propose a solution approach based on piecewise linearization of the MPCC as a mixed-integer linear program (MILP) and solving the MILP to global optimality. Non-uniform distribution of breakpoints that delimit piecewise intervals and feasibility-based domain reduction are further employed to reduce the approximation error brought by linearization. The model is implemented in a simplified Singapore network with extensive sensitivity analyses and the Sioux Falls network. Computational results demonstrate the effectiveness and efficiency of the solution approach and yield valuable insights about transportation system performance in a mixed autonomous/human driving environment.

Exploring how politicians reflect on counteracting measures: the case of the Trondheim package

Transitioning towards a low-emission society is a goal of numerous international and national political and administrative levels. This includes reducing impacts of road transport. While aiming for a transition towards more sustainable mobility, it is particularly striking that road capacity expansion is presently under planning or implementation in most main cities in Norway. Against this backdrop, we decided to study Trondheim’s Environmental Transport Package because this has a strong environmental profile with main objectives of reducing CO2 emissions and limiting car traffic, while at the same time increasing road capacity. Our exploration of how political decision-makers reflect on including road capacity expansion in a plan aimed at reducing traffic volume is only a first step towards finding out how an apparent disconnect between stated objectives and policy measures is reconciled. This might provide opportunities for better understanding and influencing politicians’ framing and actions early on, before measures are included that counteract the transition towards more sustainable mobility. We believe our explorative study reveals how motivated political decision-makers reason when faced with conflicting goals and contradictions, and, may contribute to a better understanding of what hinders transition in similar processes in other cities in Norway and abroad.

ICT Devices: Vital Tools for Enhancing Road Traffic Monitoring

Road Traffic monitoring involves the collection of data describing the characteristic of vehicles and their movement through road networks. Such data may be used for one of these purposes such as law enforcement, congestion and incident detection and increasing road capacity. Transportation is a requirement for every nation regardless of its economy, political stability, population size and technological development. Movement of goods and people from one place to another is crucial to maintain strong economic and political ties between the various components of any given nation among nations. However, there are different modes of transportation and the most paramount one to human beings is road transportation. Due to increase in the modes of transportation, road users encounter different problems such as road blockage and incidents. Therefore there is need to monitor users incidents and to know the causes. Road traffic monitoring can be done manually or using ICT devices. This paper focuses on how the use of ICT devices can enhance road traffic monitoring. It traces the brief history of transportation; it equally discussed road traffic and safety, tools for monitoring road traffic, Intelligent Transportation Systems (ITS) use for traffic monitoring and their benefits. The result shows that the use of ICT devices in road traffic monitoring should be a Millennium Goal for all developed and developing countries because of its numerous advantages in the reduction of the intensity of traffic and other road incidents.

Attack Detection and Estimation in Cooperative Vehicles Platoons: A Sliding Mode Observer Approach

Platoons of autonomous vehicles are currently being investigated by academic and industrial researchers as a way to increase road capacity and fuel efficiency. In order to fully reach such goals, a platoon must be endowed with cooperative capabilities, such as Cooperative Adaptive Cruise Control (CACC). This technique is based on the vehicles’ sensors and on wireless communication between them, in order to control their longitudinal dynamics. However, the use of wireless communication exposes individual vehicles to cyber-attacks that aim at disrupting the platoon. Detecting and estimating a class of such attacks is the challenge considered in this paper, where an adaptive sliding mode observer is designed for this purpose. Theoretical results on the observer stability and robustness and simulation results are provided.

The Impacts of Autonomous Vehicles and E-Commerce on Local Government Budgeting and Finance

Autonomous vehicles (AVs) are already being used and their proliferation is inevitable. AVs have the potential to fundamentally alter transportation systems by averting deadly crashes, providing critical mobility to the elderly and disabled, increasing road capacity, saving fuel, and lowering emissions (Fagnant and Kockelman 2015). Eighteen states and the District of Columbia have enacted legislation regarding AVs1, and the governors of four other states have signed executive orders about AVs2 (National Conference of State Legislatures 2017). In 2017 there were 33 states that had introduced AV legislation, up from 20 in 2016 (National Conference of State Legislatures 2017). As of June 2, 2017, there were 31 companies that had received permits from the California DMV to test autonomous vehicles, and the list is getting longer each month (California Department of Motor Vehicles 2017). In Berlin, Deutsche Bahn, Germany’s largest train and bus operator, is testing a driverless twelve-passenger shuttle bus (Scott 2017). Over 20 pilot or existing AV public transport programs have taken place in Europe. And the most recent AV testing permit recipient in California is a private shuttle bus operator — Bauer’s Intelligent Transportation (California Department of Motor Vehicles 2017). Much has been written about the technical challenges of integrating autonomous vehicles into traffic patterns, but to date, there has been little consideration of the significant secondary impacts that AVs present. This project aims to fill that gap. AVs have the potential to transform cities — but whether the impact is positive or not depends on how the AVs are used. If AVs use clean fuels, are used for shared rides, and become an on-demand service rather than an owned product, cities and society may benefit. Consumer-owned cars are inefficient and underused assets — most are used for less than one hour per day (OECD 2015), sitting idle for about 95 of their life, and about 10 percent of the average American’s budget goes to the cost of purchasing and fueling private vehicles (Bureau of Labor Statistics 2016). AVs will impact land use planning, transit use, government revenues, and may exacerbate societal inequality by reducing the viability of existing public transportation services. The goal of this white paper is to consider the impact of AVs on municipal budgets. AVs create a “potential rat’s nest of a budgeting challenge” (Fung 2016). This paper seeks to begin the process of untangling that rat’s nest, and provide the foundation for future phases of the project that will consider potential additional revenue sources to fund the infrastructure changes that may come from the integration of AVs as well as land use planning implications.

Convergence Analysis of Cooperative Braking Control for Interconnected Vehicle Systems

Cooperative braking control is a very important operation in vehicle platoon control for developing intelligent transportation systems, which can effectively increase road capacity, decrease safety hazard, and avoid serial rear-end collisions. This paper focuses on cooperative braking control of autonomous vehicles, the objectives of which are to ensure intervehicles keep within the safe spacing range and rapidly, smoothly, and accurately stop at the desired target stopping positions with zero velocity. To achieve these goals, a three-vehicle platoon framework is presented and the corresponding dynamic model is established based on only information about front-end and rear-end sensors of vehicles, so communication is not needed. The explicit solutions on the three-vehicle platoon are derived by rigorous convergent analysis and detailed proof, such that intrinsic characteristic of the interconnected vehicles is revealed. The resulting convergent conditions and convergent rate are obtained under three cases including: 1) considering internal virtual forces; 2) considering external braking forces; and 3) considering both the internal virtual forces and external braking forces simultaneously. Moreover, an example is further provided to verify that the derived convergence conditions are effective and sufficient under the different controller parameter selection. These results can provide a solution for a wide class of interconnected systems and a guide for controller design to select control parameters in real engineering applications. Simulation results demonstrate the validity of the proposed control approaches.

In-vehicle sensor-assisted platoon formation by utilizing vehicular communications

These days, vehicles are equipped with in-vehicle sensors so that they can collect the situation-related information, such as velocity, heading, and position. From the viewpoint of intelligent transportation system, platooning is getting more attention because of its advantages of increased road capacity, fuel saving, safety, and so on. In this article, we propose a mechanism that allows vehicles to form platoons based on their own and 1-hop neighbors’ sensory information obtained from vehicular communications. Within a platoon, the front vehicle is the leader whose role is leading the platoon, so the stability of the leader is the most critical performance factor of platooning. To achieve this, we propose to use relative mobility information of vehicles computed from the sensory information for the leader election. The performance of our platooning mechanism is evaluated by carrying out ns-3-based simulations, especially from the perspective of leader stability and road capacity.