Distributed Ordering and Optimization for Intersection Management with Connected and Automated Vehicles

Intelligent transport systems are preparing to welcome connected and automated vehicles (CAVs), although it is uncertain which algorithms should be employed for the effective and efficient management of CAV systems. Even though remarkable improvements in telecommunication technologies, such as vehicle-to-everything (V2X), enable communication and computation sharing among different agents, e.g. vehicles and infrastructures, within existing approaches, a significant part of the computation burden is still typically assigned to central units. Distributed algorithms, on the other hand, could alleviate traffic units from most, if not all, of the high dimensional calculation duties, while improving security and remaining effective. In this paper, we propose a formation-control-inspired distributed algorithm to rearrange vehicles’ passing time periods through an intersection and a novel formulation of the underlying trajectory optimization problem so that vehicles need to exchange and process only a limited amount of information. We include early simulation results to demonstrate the effectiveness of our approach

A computational framework for revealing competitive travel times with low-carbon modes based on smartphone data collection

Evaluating potential of shifting to low-carbon transport modes requires considering limited travel-time budget of travelers. Despite previous studies focusing on time-relevant modal shift, there is a lack of integrated and transferable computational frameworks, which would use emerging smartphone-based high-resolution longitudinal travel datasets. This research explains and illustrates a computational framework for this purpose. The proposed framework compares observed trips with computed alternative trips and estimates the extent to which alternatives could reduce carbon emission without a significant increase in travel time.. The framework estimates potential of substituting observed car and public-transport trips with lower-carbon modes, evaluating parameters per individual traveler as well as for the whole city, from a set of temporal and spatial viewpoints. The illustrated parameters include the size and distribution of modal shifts, emission savings, and increased active-travel growth, as clustered by target mode, departure time, trip distance, and spatial coverage throughout the city. Parameters are also evaluated based on the frequently repeated trips. We evaluate usefulness of the method by analyzing door-to-door trips of a few hundred travelers, collected from smartphone traces in the Helsinki metropolitan area, Finland, during several months. The experiment's preliminary results show that, for instance, on average, 20% of frequent car trips of each traveler have a low-carbon alternative, and if the preferred alternatives are chosen, about 8% of the carbon emissions could be saved. In addition, it is seen that the spatial potential of bike as an alternative is much more sporadic throughout the city compared to that of bus, which has relatively more trips from/to city center. With few changes, the method would be applicable to other cities, bringing possibly different quantitative results. In particular, having more thorough data from large number of participants could provide implications for transportation researchers and planners to identify groups or areas for promoting mode shift. Finally, we discuss the limitations and lessons learned, highlighting future research directions.Peer reviewe

Distributed Ordering and Optimization for Intersection Management with Connected and Automated Vehicles

Intelligent transport systems are preparing to welcome connected and automated vehicles (CAVs), although it is uncertain which algorithms should be employed for the effective and efficient management of CAV systems. Even though remarkable improvements in telecommunication technologies, such as vehicle-to-everything (V2X), enable communication and computation sharing among different agents, e.g. vehicles and infrastructures, within existing approaches, a significant part of the computation burden is still typically assigned to central units. Distributed algorithms, on the other hand, could alleviate traffic units from most, if not all, of the high dimensional calculation duties, while improving security and remaining effective. In this paper, we propose a formation-control-inspired distributed algorithm to rearrange vehicles’ passing time periods through an intersection and a novel formulation of the underlying trajectory optimization problem so that vehicles need to exchange and process only a limited amount of information. We include early simulation results to demonstrate the effectiveness of our approach

Online Set-Point Estimation for Feedback-Based Traffic Control Applications

| openaire: EC/H2020/856602/EU//FINEST TWINS Publisher Copyright: AuthorThis paper deals with traffic control at motorway bottlenecks assuming the existence of an unknown, time-varying, Fundamental Diagram (FD). The FD may change over time due to different traffic compositions, e.g., light and heavy vehicles, as well as in the presence of connected and automated vehicles equipped with different technologies at varying penetration rates, leading to inconstant and uncertain driving characteristics. A novel methodology, based on Model Reference Adaptive Control, is proposed to robustly estimate in real-time the time-varying set-points that maximise the bottleneck throughput, particularly useful when the traffic is regulated via a feedback-based controller. Furthermore, we demonstrate the global asymptotic stability of the proposed controller through a novel Lyapunov analysis. The effectiveness of the proposed approach is evaluated via simulation experiments, where the estimator is integrated into a feedback ramp-metering control strategy, employing a second-order multi-lane macroscopic traffic flow model, modified to account for time-varying FDs.Peer reviewe

Adaptive traffic control at motorway bottlenecks with time-varying Fundamental Diagram

| openaire: EC/H2020/856602/EU//FINEST TWINS Publisher Copyright: © 2021 The Authors.This paper deals with the problem of controlling traffic at motorways bottlenecks in presence of an unknown, time-varying, Fundamental Diagram (FD). The FD may change over time due to traffic composition or to the presence of Connected and Automated Vehicles (CAVs) with varying driving characteristics and penetration rates. A novel methodology, based on Model Reference Adaptive Control, is presented to robustly estimate the time-varying set-points that maximise the bottleneck throughput. The proposed approach is integrated in a control scheme that includes a linear quadratic integral regulator designed to control traffic which comprises a percentage of CAVs. Simulation experiments, based on a first-order multi-lane macroscopic traffic flow model that also considers for the capacity drop phenomenon, are presented to illustrate the effectiveness of the proposed approach. Copyright (C) 2021 The AuthorsPeer reviewe

Towards Data-Driven Vehicle Estimation for Signalised Intersections in a Partially Connected Environment

| openaire: EC/H2020/856602/EU//FINEST TWINSConnected vehicles (CVs) have the potential to collect and share information that, if appro-priately processed, can be employed for advanced traffic control strategies, rendering infrastructure-based sensing obsolete.However, before we reach a fully connected environment, where all vehicles are CVs, we have to deal with the challenge of incomplete data.In this paper, we develop data-driven methods for the estimation of vehicles approaching a signalised intersection, based on the availability of partial information stemming from an unknown penetration rate of CVs. In particular, we build machine learning models with the aim of capturing the nonlinear relations between the inputs (CV data) and the output (number of non-connected vehicles), which are characterised by highly complex interactions and may be affected by a large number of factors.We show that, in order to train these models, we may use data that can be easily collected with modern technologies. Moreover, we demonstrate that, if the available real data is not deemed sufficient, training can be performed using synthetic data, produced via microscopic simulations calibrated with real data, without a significant loss of performance. Numerical experiments, where the estimation methods are tested using real vehicle data simulating the presence of various penetration rates of CVs, show very good performance of the estimators, making them promising candidates for applications in the near future.Peer reviewe

A fairness-aware joint pricing and matching framework for dynamic ridesharing

| openaire: EC/H2020/856602/EU//FINEST TWINSEnabled by the prevailing mobile devices, novel mobility services, such as ridesharing, have a great potential to change the mobility pattern of metropolis inhabitants. In this study, we focus on the pricing and matching challenges faced by a mobility service platform when both ridesharing and non-shared mobility services are provided. A joint pricing and matching framework is proposed to efficiently dispatch vehicles and deliver explicit trip time and fare information in real-time. Besides, we define six principles to evaluate the fairness of pricing methods and develop a discount function considering the features of passengers’ shared rides. In simulation experiments where passengers can choose from different service types, we show that our method can significantly increase the system’s profit without violating the fairness principles among co-riders.Peer reviewe