Mesoscopic Traffic Simulation Research Articles

A simulation‐based approach for optimizing the placement of dedicated lanes for autonomous vehicles in large‐scale networks

AbstractThis study introduces a framework to maximize societal benefits associated with the autonomous vehicle (AV)‐dedicated lane implementation at large‐scale transportation networks, considering the travel time savings and the required investments to prepare the infrastructure for their deployment. To this end, a bi‐level optimization problem is formulated. The upper level determines the links for dedicated lane deployment, while at the lower level, a mesoscopic traffic simulation tool is employed to enable a realistic representation of these vehicles in a mixed traffic. The problem is solved using the genetic algorithm. To further reduce the computational burden, this study adopts a clustering method based on the snake algorithm to group the candidate links and reduce the size of the solution space. The proposed framework is successfully applied to the case study of Chicago downtown network, considering various demand levels, AV market penetration rates, and implementation approaches. The results highlight the need for optimizing the placement of AV‐dedicated lanes (AVDLs) to ensure the economically beneficial adoption of this strategy across different scenarios. This study provides transportation planners with key operational insights to facilitate the effective adoption of AVDLs during the transitional phase from human‐driven vehicles to a fully AV environment.

Road network free flow speed estimation using microscopic simulation and point-to-point travel times

Roads in mesoscopic traffic simulation models are often parametrized by a free speed attribute, which describes the speed at which a vehicle can traverse a road in the absence of congestion. Unlike microscopic models, mesoscopic models do not typically simulate traffic lights, individual driver behavior or acceleration and deceleration of vehicles. In consequence, the free speed parameter of each road must account for all the aforementioned effects. Modeling free speed accurately is important, as it determines the travel time, which in turn affects the decision-making of agents in the simulation.This paper introduces an approach combining microscopic simulation with machine learning models to estimate road segment free speeds. Using the microscopic simulator SUMO, we generate training data for a model search employing Bayesian optimization. Subsequently, these models undergo fine-tuning via gradient-based optimization using real-world point-to-point travel times. A significant advantage is the adaptability of this approach to diverse road networks, including those from OpenStreetMap, and the ability to incorporate routing data from various online providers independently.Our evaluation illustrates a notable decrease in prediction error between 30% and 60% compared to baseline models that assume a uniform free speed reduction for all urban roads. The fine-tuned models are able to generalize well to unseen regions and are therefore applicable to case studies where data for new or altered roads is unavailable.

Development, Calibration, and Validation of a Large-Scale Traffic Simulation Model: Belgium Road Network

Development of large-scale traffic simulation models have always been challenging for transportation researchers. One of the essential steps in developing traffic simulation models, which needs lots of resources, is travel demand modeling. Therefore, proposing travel demand models that require less data than classical travel demand models is highly important, especially in large-scale networks. This paper first presents a travel demand model named as probabilistic travel demand model, then it reports the process of development, calibration and validation of Belgium traffic simulation model. The probabilistic travel demand model takes cities' population, distances between the cities, yearly vehicle-kilometer traveled, and yearly truck trips as inputs. The extracted origin-destination matrices are imported into the SUMO traffic simulator. Mesoscopic traffic simulation and the dynamic user equilibrium traffic assignment are used to build the base case model. This base case model is calibrated using the traffic count data. Al-so, the validation of the model is performed by comparing the real (extracted from Google Map API) and simulated travel times between the cities. The validation results ensure that the model is a superior representation of reality with a high level of accuracy. The model will be helpful for road authorities, planners, and decision-makers to test different scenarios, such as the im-pact of abnormal conditions or the impact of connected and autonomous vehicles on the Belgium road network.

Optimizing vehicle dynamics co-simulation performance by introducing mesoscopic traffic simulation

Microscopic traffic simulation is often used in conjunction with vehicle dynamics simulation to test cooperative or perception-based driver assistance functions. On the other hand, visualization and the interaction of a large number of swarm vehicles is computationally burdensome, limiting the size of scenarios. To remedy this problem, this paper introduces a mesoscopic traffic model, namely the extension of the shockwave profile model to road networks, that handles traffic as continuous flows of vehicles. Adopting the idea of level of detail from computer graphics, the modeling of swarm vehicles is carried out in less detail farther away from the EGO vehicle. These levels are defined using the classical (macroscopic, mesoscopic, and microscopic) categorization of traffic modeling. The macroscopic traffic model is responsible for the traffic demand and traffic assignment. The proposed mesoscopic model is capable of capturing the fluctuating nature of traffic on lane level. Closer to the EGO vehicle microscopic traffic simulation is employed while the EGO vehicle is modeled in full-detail including vehicle dynamics too. The 3D rendering of the simulation is performed by the vehicle dynamics simulator. The challenge in the proposed methodology is transitioning between the mesoscopic and the microscopic models, i.e., selecting the boundary and spawning/destroying vehicle agents. The paper addresses this challenge with a linear (w.r.t. the vehicle number) time complexity algorithm. Practically, a dynamic downscaling of the microscopic simulation to mesoscopic level is realized outside the vicinity of the EGO vehicle. The proposed methodology is generic, and can be adapted to most existing vehicle dynamics and microscopic traffic simulator software. The solution is tested with SUMO as microsimulation and Carla as vehicle dynamics simulation through a simple path following test case in two scenarios: a congested residential area and a complex scenario with both a highway section and an urban area. Simulation results suggest that the simulation performance could be improved by 200–500% while retaining modeling accuracy, compared to the case when only microscopic simulation is used.

Developing an Agent-Based Simulator Combining Mesoscopic Traffic Simulator with Dynamic Vehicle Allocation System to Evaluate a Ride-Sharing Service in Urban Area

Developing an Agent-Based Simulator Combining Mesoscopic Traffic Simulator with Dynamic Vehicle Allocation System to Evaluate a Ride-Sharing Service in Urban Area

Investigation of the effect of autonomous vehicles (AV) on the capacity of an urban transport network

In this paper, we assess the effects of different shares of autonomous vehicles (AVs) on the traffic flow and, in particular, on the maximum possible capacity at signal-controlled intersections. For this purpose, all signal-controlled nodes in the traffic network of the Düsseldorf metropolitan area were systematically simulated and evaluated using the microscopic traffic simulation tool SUMO.The analysis shows that defensively parameterized AVs – as envisaged in the umbrella project of this research – may decrease the maximum possible traffic at signal-controlled intersections. Moreover, the simulation runs indicate that capacity at these intersections decreases almost linearly with a growing share of AV. In a second part of this analysis, a freeway section was simulated with the same varying shares of CV and AV to investigate free-flow traffic. In this case, the simulation results of the maximum traffic flow can be approximated by a third-order polynomial fit. The minimum capacity is found for the uniform share of both vehicle types (i.e. 50 % AV and 50 % CV).The overall intent of this project is to provide an approach to determine system-wide and long-term effects of AVs from local microscopic observations. To this end, the SUMO microscopic traffic simulation will be utilized to derive realistic flow capacities for signal-controlled intersections. In a next step, these capacities will be transferred to a mesoscopic traffic simulation. Subsequently, flow capacities can be systematically adjusted in this network-wide mobility simulation to parameterize the influence of future infrastructure and vehicle technologies.

Pre-study and insights to a sequential MATSim-SUMO tool-coupling to deduce 24h driving profiles for SAEVs

New mobility concepts such as shared, autonomous, electric vehicle (SAEV) fleets raise questions to the vehicles’ technical design. Compared to privately owned human driven cars, SAEVs are expected to exhibit different load profiles that entail the need for newly dimensioned powertrain and battery components. Since vehicle architecture is very sensitive to operating characteristics, detailed SAEV driving cycles are crucial for requirement engineering. As real world measurements reach their limit with new mobility concepts, this contribution seeks to evaluate three different traffic simulation approaches in their ability to model detailed SAEV driving profiles. (i) The mesoscopic traffic simulation framework MATSim is analyzed as it is predestined for large-scale fleet simulation and allows the tracking of individual vehicles. (ii) To improve driving dynamics, MATSim’s simplified velocity profiles are enhanced with real-world driving cycles. (iii) A sequential tool-coupling of MATSim with the microscopic traffic simulation tool SUMO is pursued. All three approaches are compared and evaluated by means of a comprehensive test case study. The simulation results are compared in terms of driving dynamics and energy related key performance indicators (KPI) and then benchmarked against real driving cycles. The sequential tool-coupling approach shows the greatest potential to generate reliable SAEV driving profiles.

Investigating the impacts of driving restriction on NO2 concentration by integrating citywide scale cellular data and traffic simulation

Urban traffic is one of the main sources of NO2, and driving restriction (DR) has been widely used to alleviate air pollution in China, which means that private vehicles are not allowed to enter or pass a specific area according to their last digit of license plate numbers. This study investigates the impacts of DR on NO2 concentration at the traffic analysis zone (TAZ) level, which is particularly defined for traffic-related spatial data analysis. Measured NO2 data, citywide scale cellular data, and traffic-related data were collected for analysis. Inverse distance weighting (IDW) model was used to obtain NO2 at each TAZ. The traffic model of Nanjing was built in TranStar, a mesoscopic traffic simulation platform. Based on TAZ level NO2, traffic demand, and traffic status data, a geographically weighted regression (GWR) model was developed. DR with different proportions and spatial scales were simulated in TranStar and evaluated based on the predictions of the GWR model. Results suggest that morning rush hour is the only time in the day when urban traffic is the main cause of NO2's rise. Larger traffic volume and severer congestion with lower speed lead to the higher rise of NO2. The main factors influencing the rise of NO2 could be different at distinct locations. DR strategies can decrease the rise of NO2 significantly for most TAZs. Restriction proportion has significantly higher impacts than spatial scale, and the effects of scale enlarge with the rise of proportion. A side effect of DR is that the rise of NO2 would be higher in certain regions due to travelers' mode shift and detour, which is a key point for policymakers to weigh the pros and cons.

Transitioning to a driverless city: Evaluating a hybrid system for autonomous and non-autonomous vehicles

Autonomous vehicles will transform urban mobility. However, before being fully implemented, autonomous vehicles will navigate cities in mixed-traffic roads, negotiating traffic with human-driven vehicles. In this work, we simulate a system of autonomous vehicles co-existing with human-driven vehicles, analyzing the consequences of system design choices. The system consists of a network of arterial roads with exclusive lanes for autonomous vehicles where they can travel in platoons. This paper presents the evaluation of this system in realistic scenarios evaluating the impacts of the system on travel time using mesoscopic traffic simulation. We used real data from the metropolis of São Paulo to create the simulation scenarios. The results show that the proposed system would bring reductions to the average travel time of the city commuters and other benefits such as the reduction of the space required to handle all the traffic.

Assessing the impacts of automated mobility-on-demand through agent-based simulation: A study of Singapore

• Assessed impacts of AMOD through high-fidelty activity and agent-based simulation. • Captured future demand using ABM that draws on data from smartphone-based SP survey. • Modeled operations of AMOD fleet within multimodal mesoscopic traffic simulator. • Simulated and evaluated future scenarios using a model of Singapore 2030. • Introduction of AMOD may result the cannibalization of PT and increase congestion. The advent of autonomous vehicle technologies and the emergence of new ride-sourcing business models has spurred interest in Automated Mobility-on-Demand (AMOD) as a prospective solution to meet the challenges of urbanization. AMOD has the potential of providing a convenient, reliable and affordable mobility service through more competitive cost structures enabled by autonomy (relative to existing services) and more efficient centralized fleet operations. However, the short and medium-term impacts of AMOD are as yet uncertain. On the one hand, it has the potential to alleviate congestion through increased ride-sharing and reduced car-ownership, and by complementing mass-transit. Conversely, AMOD may in fact worsen congestion due to induced demand, the cannibalization of public transit shares, and an increase in Vehicle-Kilometers Traveled (VKT) because of rebalancing and empty trips. This study attempts to systematically examine the impacts of AMOD on transportation in Singapore through agent-based simulation, modeling demand, supply and their interactions explicitly. On the demand side, we utilize an activity-based model system, that draws on data from a smartphone-based stated preferences survey conducted in Singapore. On the supply side, we model the operations of the AMOD fleet (including the assignment of requests to vehicles and rebalancing), which are integrated within a multimodal mesoscopic traffic simulator. Comprehensive simulations are conducted using a model of Singapore for the year 2030 and yield insights into the impacts of AMOD in dense transit-dependent cities from the perspective of the transportation planner, fleet operator, and user. The findings suggest that an unregulated introduction of AMOD can cause significant increases in network congestion and VKT, and have important policy implications that could potentially inform future deployments of AMOD.

Integrating Supply and Demand Perspectives for a Large-Scale Simulation of Shared Autonomous Vehicles

Transportation Network Companies (TNCs) have been steadily increasing the share of total trips in metropolitan areas across the world. Micro-modeling TNC operation is essential for large-scale transportation systems simulation. In this study, an agent-based approach for analyzing supply and demand aspects of ride-sourcing operation is done using POLARIS, a high-performance simulation tool. On the demand side, a mode-choice model for the agent and a vehicle-ownership model that informs this choice are developed. On the supply side, TNC vehicle-assignment strategies, pick-up and drop-off operations, and vehicle repositioning are modeled with congestion feedback, an outcome of the mesoscopic traffic simulation. Two case studies of Bloomington and Chicago in Illinois are used to study the framework’s computational speed for large-scale operations and the effect of TNC fleets on a region’s congestion patterns. Simulation results show that a zone-based vehicle-assignment strategy scales better than relying on matching closest vehicles to requests. For large regions like Chicago, large fleets are seen to be detrimental to congestion, especially in a future in which more travelers will use TNCs. From an operational point of view, an efficient relocation strategy is critical for large regions with concentrated demand, but not regulating repositioning can worsen empty travel and, consequently, congestion. The TNC simulation framework developed in this study is of special interest to cities and regions, since it can be used to model both demand and supply aspects for large regions at scale, and in reasonably low computational time.

Comparison of Support Vector and Non-Linear Regression Models for Estimating Large-Scale Vehicular Emissions, Incorporating Network-Wide Fundamental Diagram for Heterogeneous Vehicles

Estimation of vehicular emissions at network level is a prominent issue in transportation planning and management of urban areas. For large networks, macroscopic emission models are preferred because of their simplicity. However, these models do not consider traffic flow dynamics that significantly affect emissions production. This study proposes a network-level emission modeling framework based on the network-wide fundamental diagram (NFD), via integrating the NFD properties with an existing microscopic emission model. The NFD and microscopic emission models are estimated using microscopic and mesoscopic traffic simulation tools at different scales for various traffic compositions. The major contribution is to consider heterogeneous vehicle types with different emission generation rates in a network-level model. This framework is applied to the large-scale network of Chicago as well as its central business district. Non-linear and support vector regression models are developed using simulated trajectory data of 13 simulated scenarios. The results show a satisfactory calibration and successful validation with acceptable deviations from the underlying microscopic emissions model regardless of the simulation tool that is used to calibrate the network-level emissions model. The microscopic traffic simulation is appropriate for smaller networks, while mesoscopic traffic simulation is a proper means to calibrate models for larger networks. The proposed model is also used to demonstrate the relationship between macroscopic emissions and flow characteristics in the form of a network emissions diagram. The results of this study provide a tool for planners to analyze vehicular emissions in real time and find optimal policies to control the level of emissions in large cities.

Optimization of Actuated Traffic Signal Plans Using a Mesoscopic Traffic Simulation

AbstractTraffic signal plan designs become more complex with developments in the fields of sensing and communication and the introduction of features, such as transit priority or pedestrian actuati...

Assessing operational performance of New Zealand's South Island road network after the 2016 Kaikoura Earthquake

The Kaikoura earthquake, a 7.8 (Mw) magnitude event, occurred 2 min after midnight on 14th November 2016 NZDT, around 60 km south-west of Kaikoura at a depth of 15 km. The earthquake caused numerous landslides, many of them significant, resulting in widespread disruption and closure of sections of State Highway 1 (SH1), which is a critical corridor in the South Island of New Zealand. Post-disaster operational performance of the road network, in terms of average travel time, density, and count data, has not been assessed to date. A mesoscopic traffic simulation model of the South Island's road network was, therefore, validated against 7-day Average Daily Traffic (ADT) data starting from Day 8 after the Kaikoura earthquake. Corridor analysis and trip analysis were conducted to assess post-disaster operational performance of the road network. Corridor analysis results indicate a significant increase in traffic count and density, and a minor decrease in average travel speeds on four main corridors namely: SH65, SH63, SH6 (between SH63 and SH65), and SH7 (between SH65 and SH1), serving as the main alternative routes after the earthquake. Trip analysis results show a significant increase in the average travel time from Marlborough to other affected traffic zones (typically 20–50% and as much as 90% in the worst case) due to the increased travel distances on the alternate routes. Such information can now be used to calculate the economic impact of the Kaikoura earthquake, in terms of increased vehicle operating costs and travel time costs, by comparing the pre- and post-disaster scenarios.

A Framework for Mesoscopic Traffic Simulation in GPU

Much progress has recently been made to enhance mesoscopic traffic simulation with the focus on advanced modeling features and traffic management support capabilities. Subsequently, the computational performance needs to be improved in order to make mesoscopic traffic simulation stay effective to real-time applications. This paper presents a framework for mesoscopic traffic simulation which offloads both the demand and supply components to GPU. The simulation algorithm divides the simulation flow into different steps and designs multiple kernels to handle the steps. A high level of data parallelism is achieved by assigning the GPU threads to the appropriate components of the traffic network. Several optimization options using an innovative data structure and improved warp execution are also deployed to harness the GPU performance while preserving simulation correctness. The performance of the framework is evaluated in a real network showing the speedup of up to nearly 5 times in the demand simulation and more than 4 times in the supply simulation compared to the sequential simulation.

Comparative evaluation of measures for urban highway network resilience due to traffic incidents

Abstract The resilience of an urban highway network may be described as the ability for a highway network’s operation to adapt and rapidly recover from a disruptive event. Although the concept of resilience has been studied in the past decades, researchers have yet to agree on common measures of quantifying urban highway network resilience. This paper proposed five candidate measures of urban highway network resilience that are consistent with the concept of resilience triangle. They are derived from queue length, link speed, link travel time, frontage road delay and detour route delay, respectively. These measures were calculated from outputs of a mesoscopic traffic simulation model that mimicked the highway network in the El Paso, Texas region. Thirty scenarios were simulated, each with a complete link closure at a selected major highway location caused by a traffic incident. The results have shown that the five measures are not statistically correlated with each other, and the different measures produced different ranked lists in decreasing order of the impacts of the links closure locations. This means that using different resilience measures will lead to different conclusions in the order of disruption caused by the link closures. The outcomes supports the notion that a common measure of transportation highway resilience may not be necessary, and researchers may define their own resilience measures to meet individual project’s need.

A Mesoscopic Traffic Data Assimilation Framework for Vehicle Density Estimation on Urban Traffic Networks Based on Particle Filters

Traffic conditions can be more accurately estimated using data assimilation techniques since these methods incorporate an imperfect traffic simulation model with the (partial) noisy measurement data. In this paper, we propose a data assimilation framework for vehicle density estimation on urban traffic networks. To compromise between computational efficiency and estimation accuracy, a mesoscopic traffic simulation model (we choose the platoon based model) is employed in this framework. Vehicle passages from loop detectors are considered as the measurement data which contain errors, such as missed and false detections. Due to the nonlinear and non-Gaussian nature of the problem, particle filters are adopted to carry out the state estimation, since this method does not have any restrictions on the model dynamics and error assumptions. Simulation experiments are carried out to test the proposed data assimilation framework, and the results show that the proposed framework can provide good vehicle density estimation on relatively large urban traffic networks under moderate sensor quality. The sensitivity analysis proves that the proposed framework is robust to errors both in the model and in the measurements.

Agent-based Mesoscopic Urban Traffic Simulation based on Multi-lane Cell Transmission Model

With the increasing demand for the urban traffic simulation, the mesoscopic model takes center stage with its advantage that can simulate a large network with less computational cost and gets the information of individual vehicles. This study proposes a mesoscopic traffic simulation model called Agent-based Mesoscopic Cell Transmission Model (AMCTM). It models both longitudinal and lateral movement of vehicles. The longitudinal movement is based on the Cell Transmission Model (CTM) and implements the traffic signal by adjusting the amount of flow between cells according to the signal. The lateral movement includes mandatory lane-change to allow the vehicle to follow the specified route and the optional lane-change to reduce the travel time. To evaluate the proposed model, we conduct the experiment with a test network including signalized intersection. The result of the experiment shows that the AMCTM performs well several phenomena occur in the urban network. This study is expected to contribute to the performance evaluation of the newly developed traffic-related technologies such as autonomous vehicles and V2X communication-based control systems.

Effects of Users’ Bounded Rationality on a Traffic Network Performance: A Simulation Study

In this paper, we revisit the principle of bounded rationality applied to dynamic traffic assignment to evaluate its influences on network performance. We investigate the influence of different types of bounded rational user behavior on (i) route flows at equilibrium and (ii) network performance in terms of its internal, inflow, and outflow capacities. We consider the implementation of a bounded rational framework based on Monte Carlo simulation. A Lighthill-Whitham-Richards (LWR) mesoscopic traffic simulator is considered to calculate time-dependent route costs that account for congestion, spillback, and shock-wave effects. Network equilibrium is calculated using the Method of Successive Averages. As a benchmark, the results are compared against both Deterministic and Stochastic User Equilibrium. To model different types of bounded rational user behavior we consider two definitions of user search order (indifferent and strict preferences) and two settings of the indifference band. We also test the framework on a toy Braess network to gain insight into changes in the route flows at equilibrium for both search orders and increasing values of aspiration levels.

Predicting Air Quality by Integrating a Mesoscopic Traffic Simulation Model and Simplified Air Pollutant Estimation Models

Continuous growth in traffic demand has led to a decrease in the air quality in various urban areas. More than ever, local authorities for environmental protection and urban planners are interested in performing detailed investigations using traffic and air pollution simulations for testing various urban scenarios and raising citizen awareness where necessary. This article is focused on the traffic and air pollution in the eco-neighbourhood “Nancy Grand Cœur”, located in a medium-size city from north-eastern France. The main objective of this work is to build an integrated simulation model which would predict and visualize various environmental changes inside the neighbourhood such as: air pollution, traffic flow or meteorological information. Firstly, we conduct a data profiling analysis on the received data sets together with a discussion on the daily and hourly traffic patterns, average nitrogen dioxide concentrations and the regional background concentrations recorded in the eco-neighbourhood for the study period. Secondly, we build the 3D mesoscopic traffic simulation model using real data sets from the local traffic management centre. Thirdly, by using reliable data sets from the local air-quality management centre, we build a regression model to predict the evolution of nitrogen dioxide concentrations, as a function of the simulated traffic flow and meteorological data. We then validate the estimated results through comparisons with real data sets, with the purpose of supporting the traffic engineering decision-making and the smart city sustainability. The last section of the paper is reserved for further regression studies applied to other air pollutants monitored in the eco-neighbourhood, such as sulphur dioxide and particulate matter and a detailed discussion on benefit and challenges to conduct such studies.