Modeling the information flow propagation wave under vehicle-to-vehicle communications

Vehicle-to-vehicle (V2V) communications under the connected vehicle context have the potential to provide new paradigms to enhance the safety, mobility and environmental sustainability of surface transportation. Understanding the information propagation characteristics in space and time is a key enabler for V2V-based traffic systems. Most existing analytical models assume instantaneous propagation of information flow through multi-hop communications. Such an assumption ignores the spatiotemporal relationships between the traffic flow dynamics and V2V communication constraints. This study proposes a macroscopic two-layer model to characterize the information flow propagation wave (IFPW). The traffic flow propagation is formulated in the lower layer as a system of partial differential equations based on the Lighthill-Whitham-Richards model. Due to their conceptual similarities, the upper layer adapts and modifies a spatial Susceptible-Infected epidemic model to describe information dissemination between V2V-equipped vehicles using integro-differential equations. A closed-form solution is derived for the IFPW speed under homogeneous conditions. The IFPW speed is numerically determined for heterogeneous conditions. Numerical experiments illustrate the impact of traffic density and market penetration of V2V-equipped vehicles on the IFPW speed. The proposed model can capture the spatiotemporal relationships between the traffic and V2V communication layers, and aid in the design of novel information propagation strategies to manage traffic conditions under V2V-based traffic systems.     

A framework for the assessment of collaborative en route resource allocation strategies

Airspace Flow Programs (AFPs) assign ground delays to flights in order to limit flow through capacity constrained airspace regions. AFPs have been successful in controlling traffic with reasonable delays, but a new program called the Combined Trajectory Options Program, or CTOP, is being explored to further accommodate projected increases in traffic demand. In CTOP, centrally managed rerouting and user preference inputs are also incorporated into initial en route resource allocations. We investigate four alternative versions of resource allocation within CTOP in this research, under differing assumptions about the degree of random variability in airline flight assignment costs when measured against a simple model based upon the flight specific, but otherwise fixed, ratio of airborne flight time and ground delay unit cost. Two en route resource allocation schemes are based on ordered assignments that are similar to those used currently, and the other two are system-optimal assignment schemes. One of these system-optimal schemes is based on complete preference information, which is ideal but not realistic, and the other is based on partial information that may be feasible to implement but yields less efficient assignments. The main contribution of this research is a methodological framework to evaluate and compare these alternative en route resource allocation schemes, under varying assumptions about the information traffic managers have been provided about the flight operators’ route preferences. The framework allows us to evaluate these various schemes under differing assumptions about how well the traffic managers’ flight cost model captures flight costs. A numerical example demonstrates that a sequential resource allocation scheme – where flights are assigned resources in the order in which preference information is submitted – can be more efficient than a scheme that offers a cost minimizing allocation based on less complete preference information, and may at the same time be perceived as equitable. We also find that assigning resources in the order flights are scheduled results in less efficient allocations, but more equitable ones.     

Analytical model for information flow propagation wave under an information relay control strategy in a congested vehicle-to-vehicle communication environment

Vehicular traffic congestion in a vehicle-to-vehicle (V2V) communication environment can lead to congestion effects for information flow propagation. Such congestion effects can impact whether a specific information packet of interest can reach a desired location, and if so, in a timely manner to influence the traffic system performance. Motivated by the usefulness and timeliness of information propagation, this paper aims to characterize the information flow propagation wave (IFPW) for an information packet in a congested V2V communication environment under an information relay control strategy. This strategy seeks to exclude information that is dated in the communication buffer under a first-in, first-out queue discipline, from being relayed if the information flow regime is congested. It trades off the need to enable the dissemination of every information packet as far as possible, against the congestion effects that accrue because of the presence of multiple information packets. A macroscopic two-layer model is proposed to characterize the IFPW. The upper layer is formulated as integro-differential equations to characterize the information dissemination in space and time under this control strategy. The lower layer adopts the Lighthill-Whitham-Richards model to capture the traffic flow dynamics. Based on the upper layer model, a necessary condition is derived which quantifies the expected time length that needs to be reserved for broadcasting the information packet of interest so as to ensure the formation of an IFPW under a given density of V2V-equipped vehicles. When the necessary condition is satisfied under homogeneous conditions, it is shown that the information packet can be propagated at an asymptotic speed whose value can be derived analytically. Besides, under the proposed control strategy, only a proportion of vehicles (labeled asymptotic density of informed vehicles) can receive the specific information packet, which can be estimated by solving a nonlinear equation. The asymptotic IFPW speed, the asymptotic density of informed vehicles, and the necessary condition for the IFPW, help in evaluating the timeliness of information propagation and the influence of traffic dynamics on information propagation. In addition, the proposed model can be used to numerically estimate the IFPW speed for heterogeneous conditions, which can aid in the design of traffic management strategies built upon the timely propagation of information through V2V communication.     

Model and a solution algorithm for the dynamic resource allocation problem for large-scale transportation network evacuation

Allocating movable resources dynamically enables evacuation management agencies to improve evacuation system performance in both the spatial and temporal dimensions. This study proposes a mixed integer linear program (MILP) model to address the dynamic resource allocation problem for transportation evacuation planning on large-scale networks. The proposed model is built on the earliest arrival flow formulation that significantly reduces problem size. A set of binary variables, specifically, the beginning and the ending time of resource allocation at a location, enable a strong formulation with tight constraints. A solution algorithm is developed to solve for an optimal solution on large-scale network applications by adopting Benders decomposition. In this algorithm, the MILP model is decomposed into two sub-problems. The first sub-problem, called the restricted master problem, identifies a feasible dynamic resource allocation plan. The second sub-problem, called the auxiliary problem, models dynamic traffic assignment in the evacuation network given a resource allocation plan. A numerical study is performed on the Dallas–Fort Worth network. The results show that the Benders decomposition algorithm can solve an optimal solution efficiently on a large-scale network.     

A platoon based cooperative eco-driving model for mixed automated and human-driven vehicles at a signalised intersection

The advancements in communication and sensing technologies can be exploited to assist the drivers in making better decisions. In this paper, we consider the design of a real-time cooperative eco-driving strategy for a group of vehicles with mixed automated vehicles (AVs) and human-driven vehicles (HVs). The lead vehicles in the platoon can receive the signal phase and timing information via vehicle-to-infrastructure (V2I) communication and the traffic states of both the preceding vehicle and current platoon via vehicle-to-vehicle (V2V) communication. We propose a receding horizon model predictive control (MPC) method to minimise the fuel consumption for platoons and drive the platoons to pass the intersection on a green phase. The method is then extended to dynamic platoon splitting and merging rules for cooperation among AVs and HVs in response to the high variation in urban traffic flow. Extensive simulation tests are also conducted to demonstrate the performance of the model in various conditions in the mixed traffic flow and different penetration rates of AVs. Our model shows that the cooperation between AVs and HVs can further smooth out the trajectory of the latter and reduce the fuel consumption of the entire traffic system, especially for the low penetration of AVs. It is noteworthy that the proposed model does not compromise the traffic efficiency and the driving comfort while achieving the eco-driving strategy.     

The impacts of connected vehicle technology on network-wide traffic operation and fuel consumption under various incident scenarios

ABSTRACT

Incidents are a major source of traffic congestion and can lead to long and unpredictable delays, deteriorating traffic operations and adverse environmental impacts. The emergence of connected vehicles and communication technologies has enabled travelers to use real-time traffic information. The ability to exchange traffic information among vehicles has tremendous potential impacts on network performance especially in the case of non-recurrent congestion. To this end, this paper utilizes a microscopic simulation model of traffic in El Paso, Texas to investigate the impacts of incidents on traffic operation and fuel consumption at different market penetration rates (MPR) of connected vehicles. Several scenarios are implemented and tested to determine the impacts of incidents on network performance in an urban area. The scenarios are defined by changing the duration of incidents and the number of lanes closed. This study also shows how communication technology affects network performance in response to congestion. The results of the study demonstrate the potential effectiveness of connected vehicle technology in improving network performance. For an incident with a duration of 900?s and MPR of 80%, total fuel consumption and total travel time decreased by approximately 20%; 26% was observed in network-wide travel time and fuel consumption at 100% MPR.     

Fair dynamic resource allocation in transit-based evacuation planning

Resource allocation in transit-based emergency evacuation is studied in this paper. The goal is to find a method for allocation of resources to communities in an evacuation process which is (1) fair, (2) reasonably efficient, and (3) able to dynamically adapt to the changes to the emergency situation. Four variations of the resource allocation problem, namely maximum rate, minimum clearance time, maximum social welfare, and proportional fair resource allocation, are modeled and compared. It is shown that the optimal answer to each problem can be found efficiently. Additionally, a distributed and dynamic algorithm based on the Lagrangian dual approach, called PFD²A, is developed to find the proportional fair allocation of resources and update the evacuation process in real time whenever new information becomes available. Numerical results for a sample scenario are presented.     

Percolation phenomenon in connected vehicle network through a multi-agent approach: Mobility benefits and market penetration

This paper presents an integrated multi-agent approach, coupled with percolation theory and network science, to measure the mobility impacts (i.e., mean travel time of the system) of connected vehicle (CVtio) network at varying levels of market penetration rate. We capture the characteristics of a CV network, i.e., node degree distribution, vehicular clustering, and giant component size to verify the existence of percolation phenomenon, and further connect the emergence of mobility benefits to the percolation phase transition in the CV network. We show the percolation phase transition properties to appear in a dynamic CV network with time-correlated link and node dynamics. An analytical framework was developed to evaluate the CV network attributes with varying market penetrations (MP) and connection ranges (CR) to identify percolation phenomenon in a mixed CV and Non-CV environment. In addition, a multi-agent CV simulation platform was created to further measure (1) how varying MPs and CRs affect the network-wide mobility measured by the mean travel time of the network; and (2) when percolation transition occurs in CV network to capture the critical MP and CR. Percolation phenomenon in CV network was further validated with the analytical assessments. The results show that (1) percolation phase transition phenomenon is a function of both market penetration and communication range; (2) percolation phase transitions in both mobility and CV network are highly correlated; (3) the application can reduce the average travel time of the system by up to 20% with reasonable market penetration and communication range; (4) critical market penetration is sensitive to communication range, and vice versa; (5) at least 70% of the CVs on the network are required to show in the same cluster for mobility benefits to appear; and (6) for high levels of MP or CR, a low probability of connectivity (PC) does not dramatically change the mean travel time. These results provide solid supports to create evidence-driven frameworks to guide future CV deployment and CV network analysis.     

A real-time adaptive signal control in a connected vehicle environment

The state of the practice traffic signal control strategies mainly rely on infrastructure based vehicle detector data as the input for the control logic. The infrastructure based detectors are generally point detectors which cannot directly provide measurement of vehicle location and speed. With the advances in wireless communication technology, vehicles are able to communicate with each other and with the infrastructure in the emerging connected vehicle system. Data collected from connected vehicles provides a much more complete picture of the traffic states near an intersection and can be utilized for signal control. This paper presents a real-time adaptive signal phase allocation algorithm using connected vehicle data. The proposed algorithm optimizes the phase sequence and duration by solving a two-level optimization problem. Two objective functions are considered: minimization of total vehicle delay and minimization of queue length. Due to the low penetration rate of the connected vehicles, an algorithm that estimates the states of unequipped vehicle based on connected vehicle data is developed to construct a complete arrival table for the phase allocation algorithm. A real-world intersection is modeled in VISSIM to validate the algorithms. Results with a variety of connected vehicle market penetration rates and demand levels are compared to well-tuned fully actuated control. In general, the proposed control algorithm outperforms actuated control by reducing total delay by as much as 16.33% in a high penetration rate case and similar delay in a low penetration rate case. Different objective functions result in different behaviors of signal timing. The minimization of total vehicle delay usually generates lower total vehicle delay, while minimization of queue length serves all phases in a more balanced way.     

Instantaneous communication throughputs of vehicular ad hoc networks

Vehicular ad hoc networks (VANETs) formed by connected vehicles in a traffic stream could be applied to improve safety, mobility, and environmental impacts of a transportation system. In this paper, we present analytical models for the instantaneous communication throughputs of VANETs to measure the efficiency of information propagation under various traffic conditions at a time instant. In particular, we define broadcast and unicast communication throughputs by the wireless channel bandwith multiplied by the average probabilities that one vehicle is a successful receiver and sender in a VAENT, respectively. With a protocol communication model, we derive formulas to determine the probabilities for an equipped vehicle to be a successful broadcast receiver and a successful unicast receiver/sender, and obtain broadcast and unicast throughputs along discrete and continuous traffic streams. We further examine the impacts on communication throughputs of the transmission range and the interference range of dedicated short range communication devices as well as the market penetration rate of equipped vehicles and the percentage of senders. Finally, we investigate the influence of shock waves on communication throughputs.     

A network level connectivity robustness measure for connected vehicle environments

This study introduces a new CONnectivity ROBustness model (CONROB) to assess vehicle-to-vehicle communication in connected vehicle (CV) environments. CONROB is based on Newton’s universal law of gravitation and accounts for multiple factors affecting the connectivity in CV environments such as market penetration, wireless transmission range, spatial distribution of vehicles relative to each other, the spatial propagation of the wireless signal, and traffic density. The proposed methodology for the connectivity robustness calculation in CONROB accounts for the Link Expiration Time (LET) and the Route Expiration Time (RET) that are reflected in the stability of links between each two adjacent vehicles and the expiration time of communication routes between vehicles. Using a 117 sq-km (45-square mile) network in Washington County, located west of Portland city, Oregon, a microscopic simulation model (VISSIM) was built to verify CONROB model. A total of 45 scenarios were simulated for different traffic densities generated from five different traffic demand levels, three levels of market penetration (5%, 15%, and 25%), and three transmission range values [76 (250), 152 (500), and 305 (1000) m (ft)]. The simulation results show that the overall robustness increases as the market penetration increases, given the same transmission range, and relative traffic density. Similarly, the overall connectivity robustness increases as the relative traffic density increases for the same market penetration. More so, the connectivity robustness becomes more sensitive to the relative traffic density at higher values of transmission range and market penetration. Multiple regression analysis was conducted to show the significant effect of relative traffic density, transmission range, and market penetration on the robustness measure. The results of the study provide an evidence of the ability of the model to capture the effect of the different factors on the connectivity between vehicles, which provides a viable tool for assessing CV environments.     

Influence of connected and autonomous vehicles on traffic flow stability and throughput

The introduction of connected and autonomous vehicles will bring changes to the highway driving environment. Connected vehicle technology provides real-time information about the surrounding traffic condition and the traffic management center’s decisions. Such information is expected to improve drivers’ efficiency, response, and comfort while enhancing safety and mobility. Connected vehicle technology can also further increase efficiency and reliability of autonomous vehicles, though these vehicles could be operated solely with their on-board sensors, without communication. While several studies have examined the possible effects of connected and autonomous vehicles on the driving environment, most of the modeling approaches in the literature do not distinguish between connectivity and automation, leaving many questions unanswered regarding the implications of different contemplated deployment scenarios. There is need for a comprehensive acceleration framework that distinguishes between these two technologies while modeling the new connected environment. This study presents a framework that utilizes different models with technology-appropriate assumptions to simulate different vehicle types with distinct communication capabilities. The stability analysis of the resulting traffic stream behavior using this framework is presented for different market penetration rates of connected and autonomous vehicles. The analysis reveals that connected and autonomous vehicles can improve string stability. Moreover, automation is found to be more effective in preventing shockwave formation and propagation under the model’s assumptions. In addition to stability, the effects of these technologies on throughput are explored, suggesting substantial potential throughput increases under certain penetration scenarios.     

Accurate and cost-effective traffic information acquisition using adaptive sampling: Centralized and V2V schemes

The new generation of GPS-based tolling systems allow for a much higher degree of road sensing than has been available up to now. We propose an adaptive sampling scheme to collect accurate real-time traffic information from large-scale implementations of on-board GPS-based devices over a road network. The goal of the system is to minimize the transmission costs over all vehicles while satisfying requirements in the accuracy and timeliness of the traffic information obtained. The system is designed to make use of cellular communication as well as leveraging additional technologies such as roadside units equipped with WiFi and vehicle-to-vehicle (V2V) dedicated short-range communications (DSRC). As opposed to fixed sampling schemes, which transmit at regular intervals, the sampling policy we propose is adaptive to the road network and the importance of the links that the vehicle traverses. Since cellular communications are costly, in the basic centralized scheme, the vehicle is not aware of the road conditions on the network. We extend the scheme to handle non-cellular communications via roadside units and vehicle-to-vehicle (V2V) communication. Under a general traffic model, we prove that our scheme always outperforms the baseline scheme in terms of transmission cost while satisfying accuracy and real-time requirements. Our analytical results are further supported via simulations based on actual road networks for both the centralized and V2V settings.     

Generic model for resource allocation in transportation. Application to urban parking management

In this paper, we define the online localized resource allocation problem, especially relevant for modeling transportation applications. The problem modeling takes into account simultaneously the geographical location of consumers and resources together with their online nondeterministic appearance. We use urban parking management as an illustration of this problem. In fact, urban parking management is an online localized resource allocation problem, where the question is how to find an efficient allocation of parking spots to drivers, while they all have dynamic geographical positions and appear nondeterministically. We define this problem and propose a multiagent system to solve it. The objective of the system is to decrease, for private vehicles drivers, the parking spots search time. The drivers are organized in communities and share information about spots availability. We have defined two cooperative models and compared them: a fully cooperative model, where agents share all the available information, and a “coopetitive” model, where drivers do not share information about the spot that they have chosen. Results show the superiority of the first model.     

Dynamic stochastic transit assignment with explicit seat allocation model

This paper proposes a stochastic dynamic transit assignment model with an explicit seat allocation process. The model is applicable to a general transit network. A seat allocation model is proposed to estimate the probability of a passenger waiting at a station or on-board to get a seat. The explicit seating model allows a better differentiation of in-vehicle discomfort experienced by sitting and standing passengers. The paper proposes simulation procedures for calculating the sitting probability of each type of passengers. A heuristic solution algorithm for finding an equilibrium solution of the proposed model is developed and tested. The numerical tests show significant influences of the seat allocation model on equilibrium departure time and route choices of passengers. The proposed model is also applied to evaluate the effects of an advanced public transport information system (APTIS) on travellers’ decision-making.     

Constraint handling methods in pavement maintenance programming

The problem of pavement maintenance management at the network level is one of maintaining as high a level of serviceability as possible for a pavement network system through reactive and proactive repair actions, whilst optimising the use of available resources. This problem has traditionally been solved using techniques like mathematical programming and heuristic methods. Lately, the use of genetic algorithms (GAs) to solve resource allocation problems like the network pavement maintenance problem has received increased attention from researchers. GAs have been demonstrated to be better than traditional techniques in terms of solution quality and diversity. However, the performance of the GAs is affected by the method used to handle the many constraints present in the formulation of such resource allocation methods. Penalty as well as generate and repair methods are the usual techniques used to handle constraints, but these have their drawbacks in terms of computational efficiency and tendency to get trapped in sub-optimal solution spaces. The paper proposes a third method that is computationally more efficient than the previous methods. The method is based on prioritised allocation of resources to maintenance activities and the maximum utilisation of resources. Constraints on maximum resource availability are no longer used passively to check on solution feasibility (as in the previous methods) but are used to help generate feasible solutions during the resource allocation phase of the algorithm itself. It is demonstrated that the GA with the prioritised resource allocation method (PRAM) outperforms the traditional GA with repair or penalty methods. PRAM was able to consistently outperform the other two GA based methods, both in terms of solution quality as well as computational time. It is concluded that PRAM can be used as the basis of more efficient resource allocation procedures in the area of pavement maintenance management.     

A data fusion approach for real-time traffic state estimation in urban signalized links

Real-time estimation of the traffic state in urban signalized links is valuable information for modern traffic control and management. In recent years, with the development of in-vehicle and communication technologies, connected vehicle data has been increasingly used in literature and practice. In this work, a novel data fusion approach is proposed for the high-resolution (second-by-second) estimation of queue length, vehicle accumulation, and outflow in urban signalized links. Required data includes input flow from a fixed detector at the upstream end of the link as well as location and speed of the connected vehicles. A probability-based approach is derived to compensate the error associated with low penetration rates while estimating the queue tail location, which renders the proposed methodology more robust to varying penetration rates of connected vehicles. A well-defined nonlinear function based on traffic flow theory is developed to attain the number of vehicles inside the queue based on queue tail location and average speed of connected vehicles. The overall scheme is thoroughly tested and demonstrated in a realistic microscopic simulation environment for three types of links with different penetration rates of connected vehicles. In order to test the efficiency of the proposed methodology in case that data are available at higher sampling times, the estimation procedure is also demonstrated for different time resolutions. The results demonstrate the efficiency and accuracy of the approach for high-resolution estimation, even in the presence of measurement noise.     

Personalized real-time traffic information provision: Agent-based optimization model and solution framework

The advancement of information and communication technology allows the use of more sophisticated information provision strategies for real-time congested traffic management in a congested network. This paper proposes an agent-based optimization modeling framework to provide personalized traffic information for heterogeneous travelers. Based on a space–time network, a time-dependent link flow based integer programming model is first formulated to optimize various information strategies, including elements of where and when to provide the information, to whom the information is given, and what alternative route information should be suggested. The analytical model can be solved efficiently using off-the-shelf commercial solvers for small-scale network. A Lagrangian Relaxation-based heuristic solution approach is developed for medium to large networks via the use of a mesoscopic dynamic traffic simulator.     

A model of daily time allocation to discretionary out-of-home activities and trips

This paper presents a model of discrete activity choice and continuous resource allocation which is based on the premise of random utility maximization and which can be conveniently estimated using existing statistical software packages. The model derivation involves virtually no approximations and adheres strictly to the utility maximization concept. The empirical analysis applies the model to the participation choice and resource (time) allocation to nonwork, out-of-home activities by workers. The statistical results show that activity choice and time allocation are governed by the same mechanism as the utilitarian assumptions indicate and support the theoretical framework employed in the model development.     

Deriving macroscopic fundamental diagrams from probe data: Issues and proposed solutions

Well-defined relationships between flow and density averaged spatially across urban traffic networks, more commonly known as Macroscopic Fundamental Diagrams (MFDs), have been recently verified to exist in reality. Researchers have proposed using MFDs to monitor the status of urban traffic networks and to inform the design of network-wide traffic control strategies. However, it is also well known that empirical MFDs are not easy to estimate in practice due to difficulties in obtaining the requisite data needed to construct them. Recent works have devised ways to estimate a network’s MFD using limited trajectory data that can be obtained from GPS-equipped mobile probe vehicles. These methods assume that the market penetration level of mobile probe vehicles is uniform across the entire set of OD pairs in the network; however, in reality the probe vehicle market penetration rate varies regionally within a network. When this variation is combined with the imbalance of probe trip lengths and travel times, the compound effects will further complicate the estimation of the MFD.To overcome this deficit, we propose a method to estimate a network’s MFD using mobile probe data when the market penetration rates are not necessarily the same across an entire network. This method relies on the determination of appropriate average probe penetration rates, which are weighted harmonic means using individual probe vehicle travel times and distances as the weights. The accuracy of this method is tested using synthetic data generated in the INTEGRATION micro-simulation environment by comparing the estimated MFDs to the ground truth MFD obtained using a 100% market penetration of probe vehicles. The results show that the weighted harmonic mean probe penetration rates outperform simple (arithmetic) average probe penetration rates, as expected. This especially holds true as the imbalance of demand and penetration level increases. Furthermore, as the probe penetration rates are generally not known, an algorithm to estimate the probe penetration rates of regional OD pairs is proposed. This algorithm links count data from sporadic fixed detectors in the network to information from probe vehicles that pass the detectors. The simulation results indicate that the proposed algorithm is very effective. Since the data needed to apply this algorithm are readily available and easy to collect, the proposed algorithm is practically feasible and offers a better approach for the estimation of the MFD using mobile probe data, which are becoming increasingly available in urban environments.