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Trip information and navigation systems are expected to become key components of future traffic management strategies, which, if properly exploited, may contribute to the mitigation of car usage externalities. In this study, we investigate social routing recommendations, which could be associated with nudges, and delivered via a navigation app, aiming at promoting sustainable routing behavior, where some drivers are asked to take longer routes and make travel time sacrifices (TTS). In particular, we propose a framework including data collection and behavioral modeling to identify the impacts of various types of information delivered to drivers, goals of the detour, and personal characteristics on drivers' TTS behavior. The methodology includes stated choice and revealed choice experiments in two European cities, Amsterdam and Helsinki, and a mixed ordered-response logit model to provide insights into TTS behavior. Our analyses show that delivering different information and nudges results in different levels of TTS. However, regardless of the goal of the detour, offering incentives to drivers enables achieving a higher level of TTS. Comparing the stated and revealed data, regarding TTS and compliance rate, also clarifies significant differences between these two types of data. • We investigate social routing recommendations delivered via a navigation app. • A framework including data collection (SP and RP) and behavioral modeling is designed. • Factors affecting drivers' travel time sacrifice behavior are identified. • Delivering different information and nudges results in different levels of travel time sacrifice. • The provided insights can be used to develop future mobility management strategies. [ABSTRACT FROM AUTHOR]

Mobility-on-demand (MoD) has the potential to revolutionise the patterns of urban mobility. Typically, an MoD platform provides both ride-hailing and ridesharing services, exacerbating the challenges of operating a city-scale real-time MoD system. Existing studies assume that travellers are fully compliant with the platform's decisions regarding pricing and vehicle assignments, whereas, in reality, travellers can choose different modes based on monetary costs and travel experience, which may conflict with the results derived from the system perspective. In this study, we relax this assumption by accounting for pricing fairness and the travellers' modal choices within a framework designed to optimise vehicle–traveller matching when both ride-hailing and ridesharing services are provided by an MoD platform. Six fairness principles are defined to characterise fair pricing for shared rides. Computationally efficient optimisation problems are formulated accounting for co-existing ride-hailing and ridesharing services. In numerical experiments, we assess the effectiveness of our method and compare it with state-of-the-art ones using a dataset of taxi requests for New York City. The results show that our optimisation strategy can significantly increase the service ratio and profit without sacrificing the service quality. • A fair-price-based, real-time ridesharing matching. • An optimization model that accounts for the traveler's mode choice. • Both ride-hailing and ridesharing service are considered. • Definition of six fair pricing principles for ridesharing trips. [ABSTRACT FROM AUTHOR]

The rapid development of information and communications technologies acts as an enabler for the successful implementation of vehicle automation and advanced traffic management applications. In particular, the appearance of new sources of high-resolution trajectory data, such as videos obtained from drones, provides an opportunity to build accurate maps and enrich applications in traffic research at an unprecedented resolution. However, existing methods cannot handle certain features, such as, among others, accurate of road lane identification. This paper proposes a constrained spectral clustering method to identify lane information from high-resolution trajectory data. Contrary to state-of-the-art methods, such as the Gaussian mixture model, the proposed method is directly applicable to two-dimensional trajectory data, without assuming a constant number of lanes characterised by the same lane width. The trajectory data is clustered by taking into account the neighbourhood distances and prior knowledge via defining so-called must-link and cannot-link constraints, which significantly improve the clustering results, especially in cases where the number of lanes or the lane width changes. The proposed method has been evaluated through numerical experiments using data obtained from drone videos, and the results indicate that the method performs well on complex road segments, even in the presence of a varying number of lanes or lane-changing manoeuvres. • Semi-supervised method identifies lanes from trajectory data without external sources. • Constrained spectral clustering incorporates prior trajectory knowledge. • Handles heterogeneous road segments in shapes, lanes, and widths. • Utilises 2D trajectory data, unlike prior studies using lateral distance. [ABSTRACT FROM AUTHOR]

A Model Predictive Control (MPC) strategy for motorway traffic management, which takes into account both conventional control measures and control actions executed by vehicles equipped with Vehicle Automation and Communication Systems (VACS), is presented and evaluated using microscopic traffic simulation. A stretch of the motorway A20, which connects Rotterdam to Gouda in the Netherlands, is taken as a realistic test bed. In order to ensure the reliability of the application results, extensive speed and flow measurements, collected from the field, are used to calibrate the site’s microscopic traffic simulation model. The efficiency of the MPC framework, applied to this real sizable and complex network under realistic traffic conditions, is examined for different traffic conditions and different penetration rates of equipped vehicles. The adequacy of the control application when only VACS equipped vehicles are used as actuators, is also considered, and the related findings underline the significance of conventional control measures during a transition period or in case of increased future demand. [ABSTRACT FROM AUTHOR]

This study presents a thorough microscopic simulation investigation of a recently developed model-based approach for per-lane density estimation, as well as on-ramp and off-ramp flow estimation, for highways in the presence of connected vehicles. The estimation methodology is mainly based on the assumption that a certain percentage of vehicles is equipped with Vehicle Automation and Communication Systems (VACS), which provide the necessary measurements used by the estimator, namely vehicle speed and position measurements. In addition, a minimum number of conventional flow detectors is needed. In the investigation, a calibrated and validated, with real data, microscopic multi-lane model is employed, which concerns a stretch of motorway A20 from Rotterdam to Gouda in the Netherlands. It is demonstrated that the proposed methodology provides satisfactory estimation performance even for low penetration rates of connected vehicles, while it is also shown that the method is little sensitive to the parameters (two in total) of the model utilized by the estimator. [ABSTRACT FROM AUTHOR]

A model-based traffic state estimation approach is developed for per-lane density estimation as well as on-ramp and off-ramp flows estimation for highways in presence of connected vehicles. Three are the basic ingredients of the developed estimation scheme: (1) a data-driven version of the conservation-of-vehicles equation (in its time- and space-discretized form); (2) the utilization of position and speed information from connected vehicles’ reports, as well as total flow measurements obtained from a minimum number (sufficient for the observability of the model) of fixed detectors, such as, for example, at the main entry and exit of a given highway stretch; and (3) the employment of a standard Kalman filter. Furthermore, necessary and sufficient conditions for the (strong) structural observability of the introduced model are established (properties, which are rarely studied in the literature on traffic estimation), which yield the fixed detectors requirements needed for the proper operation of the developed estimation scheme. The performance of the estimation scheme is evaluated for various penetration rates of connected vehicles utilizing real microscopic traffic data collected within the Next Generation SIMulation (NGSIM) program. It is shown that the estimation performance is satisfactory, in terms of a suitable metric, even for low penetration rates of connected vehicles. The sensitivity of the estimation performance to variations of the model parameters (two in total) is also quantified, and it is shown that, overall, the estimation scheme is little sensitive to the model parameters. [ABSTRACT FROM AUTHOR]

• Knowledge about on-board passenger comfort in public transport vehicles is key to preventing crowding and, in particular, restoring passenger confidence post-COVID-19. • Automatic Passenger Counting systems offer relevant but usually incomplete data due to the high installation costs involved. • A new Kalman filter based estimation method for on-board comfort is proposed, using historical and current non-exhaustive Automatic Passenger Counting data. • The accuracy and reliability of the method are evaluated through application to the tramway network of Nantes, France. • The method is found to deliver good estimation accuracy in terms of both passenger numbers and on-board comfort levels. The prevention of crowding inside buses, trams and trains is an important component of on-board passenger comfort and is central to the provision of good public transport services. In light of the COVID-19 pandemic and the associated significant reduction in public transport patronage and, more importantly, in passenger confidence, the avoidance of crowds by passengers and operators alike becomes even more critical. This is where the provision of information on on-board comfort becomes a necessity. The present study, therefore, proposes a new Kalman filter based estimation scheme for on-board comfort levels, employing historical and current (same-day) non-exhaustive Automatic Passenger Counting data, as well as Automatic Vehicle Locating measurements. The accuracy and reliability of the estimation is, then, evaluated through application to the tramway network of the French city of Nantes. The results suggest that the proposed method is able to deliver good estimation accuracy, both in terms of absolute passenger numbers, but also, more crucially, in terms of on-board comfort Levels of Service. [ABSTRACT FROM AUTHOR]

A widespread deployment of vehicle automation and communication systems (VACS) is expected in the next years. This may lead to improvements in traffic management efficiency because of the novel possibilities of using VACS both as sensors and as actuators, as well as of a variety of new communications channels (vehicle-to-vehicles, vehicle-to-infrastructure) and related opportunities. To achieve this traffic flow efficiency, appropriate studies, developing potential control strategies to exploit the VACS availability, are essential. This paper describes a hierarchical model predictive control framework that can be used for the coordinated and integrated control of a motorway system, considering that an amount of vehicles are equipped with specific VACS. The concept employs and exploits the synergistic (integrated) action of a number of old and new control measures, including ramp metering, vehicle speed control, and lane changing control at a macroscopic level. The effectiveness and the computational feasibility of the proposed approach are demonstrated via microscopic simulation for a variety of penetration rates of equipped vehicles. [ABSTRACT FROM AUTHOR]

Proposed or emerging vehicle automation and communication systems (VACS) may contribute to the mitigation of motorway traffic congestion on the basis of appropriate traffic control strategies. In this context, this paper presents a novel first-order multi-lane macroscopic traffic flow model for motorways which is mainly intended for use within a related optimal control problem formulation. The model’s starting point is close to the well-known CTM (cell-transmission model), which is modified and extended to consider additional aspects of the traffic dynamics, such as lane changing and the capacity drop, via appropriate procedures for computing lateral and longitudinal flows. The model has been derived with a view to combine realistic traffic flow description with a simple (linear or piecewise linear) mathematical form, which can be exploited for efficient optimal control problem formulations, as described in a companion (Part II) paper. Although the model has been primarily derived for use in future traffic conditions including VACS, it may also be used for conventional traffic flow representation. In fact, the accuracy of the proposed modelling approach is demonstrated through calibration and validation procedures using real data from an urban motorway located in Melbourne, Australia. [ABSTRACT FROM AUTHOR]

Integrated motorway traffic flow control considering the use of Vehicle Automation and Communication Systems (VACS) is considered in this paper. VACS may act both as sensors (providing information on traffic conditions) and as actuators, permitting the deployment of ramp metering, variable speed limits, and lane changing control. The integrated traffic control problem is addressed through the formulation of a linearly constrained optimal control problem based on the first-order multi-lane model for motorways introduced and validated in a companion paper (Part I). A case study illustrating the potential improvements achievable using this approach is presented. [ABSTRACT FROM AUTHOR]

On-demand ridesharing has been recognised as an effective way to meet travel needs while significantly reducing the number of required vehicles. As the growth of on-demand mobility services and the advent of shared autonomous vehicles are envisioned to boost the presence of ridesharing vehicles, these may soon significantly affect traffic patterns in our cities. However, most previous studies investigating dynamic ridesharing systems overlook the effects on travel times due to the assignment of requests to vehicles and their routes. In order to assign the ridesharing vehicles while considering network traffic dynamics, we propose a strategy that incorporates time-dependent link travel time predictions into the request–vehicle assignment to avoid or mitigate traffic congestion. In particular, we formulate an efficient linear assignment problem that considers multiple path alternatives and accounts for the impact on travel times, which may be potentially caused by vehicles assigned to specific routes. Simulation experiments reveal that using an appropriate congestion avoidance ridesharing strategy can remarkably reduce passenger average travel and waiting times by alleviating endogenous congestion caused by ridesharing fleets. • A scalable dynamic ridesharing method that accounts for endogenous congestion effects. • Vehicle assignment and routing are optimised simultaneously. • Computationally efficient decentralised framework allowing privacy preservation. • Well operated real-time ridesharing benefits the traffic system performance. [ABSTRACT FROM AUTHOR]

First-order traffic flow models of the LWR (Lighthill-Whitham-Richards) type are known for their simplicity and computational efficiency and have, for this reason, been widely used for various traffic engineering tasks. However, these first-order models are not able to reproduce significant traffic phenomena of great interest, such as the capacity drop and stop-and-go waves. This paper presents an overview of modeling approaches, which introduce the ability to reflect the capacity-drop phenomenon into discretized LWR-type first-order traffic flow models; and also proposes a new approach. The background and main characteristics of each approach are analyzed with particular emphasis on the practical applicability of such models for traffic simulation, management and control. The presented modeling approaches are tested and validated using real data from a motorway network in the U.K. [ABSTRACT FROM AUTHOR]

This paper presents a thorough microscopic simulation investigation of a recently proposed methodology for highway traffic estimation with mixed traffic, i.e., traffic comprising both connected and conventional vehicles, which employs only speed measurements stemming from connected vehicles and a limited number (sufficient to guarantee observability) of flow measurements from spot sensors. The estimation scheme is tested using the commercial traffic simulator Aimsun under various penetration rates of connected vehicles, employing a traffic scenario that features congested as well as free-flow conditions. The case of mixed traffic comprising conventional and connected vehicles equipped with adaptive cruise control, which feature a systematically different car-following behavior than regular vehicles, is also considered. In both cases, it is demonstrated that the estimation results are satisfactory, even for low penetration rates. [ABSTRACT FROM AUTHOR]

The objective of this paper is the regulation of freeway traffic by means of optimal control techniques. A first innovative aspect of the proposed approach is the adopted objective function in which, besides the reduction of traffic congestion (which is typically considered in traffic control schemes), the minimization of traffic emissions is also included. Moreover, a multi-class framework is defined in which two classes of vehicles (cars and trucks) are explicitly modelled, and specific control actions for each vehicle class are sought. This results in the formulation of a multi-objective optimal control problem which is described in the paper and for which a specific solution algorithm is developed and used. The algorithm exploits a specific version of the feasible direction algorithm whose effectiveness is demonstrated in the paper by means of simulation results. [ABSTRACT FROM AUTHOR]

While passenger cars offer the convenience of door-to-door travel, they are responsible for a substantial share of urban traffic-related carbon emissions, accounting for 44% of the global transport sector's emissions in 2022. Existing studies have not adequately identified the driving factors and spatiotemporal patterns of Passenger car CO2 Emissions (PCEs). To fill this gap, a comprehensive analytical framework for analysing PCE, encompassing several key components, has been developed. First, the PCE is estimated at a fine-grained level by utilising million-scale GPS trajectory data. Secondly, road network and urban functional features are extracted based on multi-source data, examining their nonlinear impacts on PCE. The driving factors of PCE during peak and off-peak periods are identified based on interpretable factor analysis. Finally, latent space clustering techniques are applied to reveal the spatiotemporal patterns of PCE, verified by significance tests. The effectiveness of this framework is demonstrated via a case study in Hangzhou, China. Notably, during peak hours, the driving factors of PCE are the road network's closeness centrality, service-related Points of Interest (POI) density, and trunk road density. In addition, five distinctive PCE patterns are identified. These findings facilitate the development of targeted and differentiated passenger car traffic management strategies, promoting carbon emission reduction in the transport sector. [Display omitted] • A comprehensive analytical framework for PCE is developed. • The impact of road network and urban functional features on PCE is considered. • The driving factors and spatiotemporal patterns of PCE are identified. [ABSTRACT FROM AUTHOR]

Traffic congestion on motorways is a serious threat for the economic and social life of modern society as well as for the environment, which calls for drastic and radical solutions. Conventional traffic management measures, currently applied, are valuable, but face limitations. During the last decades, a variety of vehicle automation and communication systems (VACS) have been developed and deployed, and many more are expected to appear in the near future. These systems provide a novel basis for a new generation of traffic management, which exploits emerging vehicle automation functions and connectivity channels to enable sensible traffic flow improvements in terms of efficiency and safety. A number of innovative concepts, tools and results that open up new horizons for traffic management research and practice in presence of VACS have been produced recently in the frame of TRAMAN21, an ERC Advanced Grant. This paper presents a collection of novel problems as well as of related traffic flow modelling, estimation and control developments for motorway traffic that can be used in the evolving traffic environment with VACS. [ABSTRACT FROM AUTHOR]

This paper presents a safety-enhanced eco-driving strategy for connected and autonomous vehicles (CAVs), which is implemented by a hierarchical and distributed framework. The driving risk field, shockwave theory, and motion planning and control method are integrated into this framework to optimize the trajectories of CAVs on a signalized arterial under mixed traffic flow, with the aim of reducing the driving risk and fuel consumption of CAVs simultaneously, while ensuring traffic efficiency. The optimization procedure is mainly composed of two parts: long-term trajectory planning based on optimal control and short-term trajectory control based on model predictive control, which makes the strategy more adaptable to the various traffic conditions. The results show that the proposed framework can effectively reduce the safety risk that vehicles are exposed to and their fuel consumption by 18%–24% and 20%–27%, respectively. Furthermore, it reveals that conventional eco-driving strategies may result in negative safety issues when only considering the impact of preceding vehicles on the eco-CAV. However, these negative impacts can be eliminated when the impacts of following vehicles on the eco-CAV are taken into account. In addition, the sensitivity analysis on the Market Penetration Rate (MPR) of CAVs and traffic demand is performed. The results show that the framework is robust and can work under various traffic conditions (including under-saturated and over-saturated ones) and different MPRs. • Propose a safety-enhanced eco-driving strategy for CAVs under mixed traffic flow. • Develop two algorithms to alleviate intersection queuing effects for eco-driving. • While ensuring traffic efficiency, optimize CAVs' fuel consumption and driving risk. • Perform numerical experiments to assess the benefits of the proposed framework. • Reveal conventional eco-driving strategy may result in safety risks. [ABSTRACT FROM AUTHOR]

In this paper we develop a fast model predictive control (MPC) approach for variable speed limit coordination to resolve freeway jam waves. Existing MPC approaches that are based on the second-order traffic flow models suffer from high computation load due to the non-linear and non-convex optimization formulation. In recent years, simplified MPC approaches which are based on discrete first-order traffic flow models have attracted more and more attention because they are beneficial for real-time applications. In literature, the type of traffic jam resolved by these approaches is limited to the standing queue in which the jam head is fixed at the bottleneck. Another type of traffic jam known as the jam wave, has been neglected by the discrete first-order model-based MPC approaches. To fill this gap, we develop a fast MPC approach based on a more accurate discrete first-order model. The model keeps the linear property of the classical discrete first-order model, meanwhile takes traffic flow features of jam waves propagation into consideration. A classical non-linear MPC and a recently proposed linear MPC are compared with the proposed MPC in terms of computation speed and jam wave resolution by a benchmark problem. Simulation results show that the proposed MPC resolves the jam wave with a real-time feasible computation speed. [ABSTRACT FROM AUTHOR]