Showing papers on "Traffic simulation published in 2011"

SUMO - Simulation of Urban MObility An Overview

Abstract: SUMO is an open source traffic simulation package including net import and demand modeling components. We describe the current state of the package as well as future developments and extensions. SUMO helps to investigate several research topics e.g. route choice and traffic light algorithm or simulating vehicular communication. Therefore the framework is used in different projects to simulate automatic driving or traffic management strategies.

Dynamic Ride-Sharing: a Simulation Study in Metro Atlanta

Abstract: Smartphone technology enables dynamic ride-sharing systems that bring together people with similar itineraries and time schedules to share rides on short-notice. This paper considers the problem of matching drivers and riders in this dynamic setting. We develop optimization-based approaches that aim at minimizing the total system-wide vehicle miles incurred by system users, and their individual travel costs. To assess the merits of our methods we present a simulation study based on 2008 travel demand data from metropolitan Atlanta. The simulation results indicate that the use of sophisticated optimization methods instead of simple greedy matching rules substantially improve the performance of ride-sharing systems. Furthermore, even with relatively low participation rates, it appears that sustainable populations of dynamic ride-sharing participants may be possible even in relatively sprawling urban areas with many employment centers.

On the assessment of vehicle trajectory data accuracy and application to the Next Generation SIMulation (NGSIM) program data

Abstract: Trajectories drawn in a common reference system by all the vehicles on a road are the ultimate empirical data to investigate traffic dynamics. The vast amount of such data made freely available by the Next Generation SIMulation (NGSIM) program is therefore opening up new horizons in studying traffic flow theory. Yet the quality of trajectory data and its impact on the reliability of related studies was a vastly underestimated problem in the traffic literature even before the availability of NGSIM data. The absence of established methods to assess data accuracy and even of a common understanding of the problem makes it hard to speak of reproducibility of experiments and objective comparison of results, in particular in a research field where the complexity of human behaviour is an intrinsic challenge to the scientific method. Therefore this paper intends to design quantitative methods to inspect trajectory data. To this aim first the structure of the error on point measurements and its propagation on the space travelled are investigated. Analytical evidence of the bias propagated in the vehicle trajectory functions and a related consistency requirement are given. Literature on estimation/filtering techniques is then reviewed in light of this requirement and a number of error statistics suitable to inspect trajectory data are proposed. The designed methodology, involving jerk analysis, consistency analysis and spectral analysis, is then applied to the complete set of NGSIM databases.

Assessment of the impact of speed limit reduction and traffic signal coordination on vehicle emissions using an integrated approach

Abstract: This paper examines the effects of two traffic management measures, speed limit reduction and coordinated traffic lights, in an area of Antwerp, Belgium. An integrated model is deployed that combines the microscopic traffic simulation model Paramics with the CO2 and NOX emission model VERSIT+. On the one hand, reductions in CO2 and NOX emissions of about 25% were found if speed limits are lowered from 50 to 30 km/h in the residential part of the case study area. On the other hand, reductions in the order of 10% can be expected from the implementation of a green wave signal coordination scheme along an urban arterial road.

Dynamic Traffic Assignment Model for Highly Congested Urban Networks

Abstract: The management of severe congestion in complex urban networks calls for dynamic traffic assignment (DTA) models that can replicate real traffic situations with long queues and spillbacks. DynaMIT-P, a mesoscopic traffic simulation system, was enhanced and calibrated to capture the traffic characteristics in the city of Beijing, China. All demand and supply inputs and parameters were calibrated simultaneously using sensor counts and floating car travel time data. Successful calibration was achieved with the Path-Size Logit route choice model, which accounts for overlapping routes. Furthermore, explicit representations of lane groups were required to properly model the traffic delays and queues. A modified treatment of acceptance capacity was required to model the large number of short links in the transportation network (close to the length of one vehicle). In addition, the impacts of bicycles and pedestrians on auto traffic were modeled by dynamic road segment capacities. It is found that the synthesis of solutions to those individual issues is crucial for the successful calibration of such a complicated traffic network.

Traffic Signal Optimization with Application of Transit Signal Priority to an Isolated Intersection

Abstract: Transit signal priority (TSP) is a control strategy that has been used extensively to improve transit operations in urban networks. However, several issues related to TSP deployment—including the effect of TSP on auto traffic and the provision of priority to transit vehicles traveling in conflicting directions at traffic signals—have not yet been addressed satisfactorily by existing control systems. This paper presents a real-time, traffic-responsive signal control system for signal priority on conflicting transit routes that also minimizes the negative effects on auto traffic. The proposed system determines the signal settings that minimize the total person delay in the network while assigning priority to the transit vehicles on the basis of their passenger occupancy. The system was tested through simulation at a complex signalized intersection located in Athens, Greece, that had heavy traffic demands and multiple bus lines traveling in conflicting directions. Results showed that the proposed system led ...

Electric vehicle simulator for energy consumption studies in electric mobility systems

Abstract: One of the most important environmental problems in large cities is the vehicular emission. Electric Vehicles (EVs) are a growing alternative for internal combustion engine (ICE) vehicles. Since this kind of vehicle has low autonomy yet, it is important to optimize energy consumption, for instance by planning a suitable infrastructure of battery recharge and/or battery-switch stations. This paper presents an architecture for EV simulation, important to analyze traffic flow, its dynamics and the performance when there are obstructions or intense traffic. There are several tools for traffic simulation, SUMO (Simulation of Urban MObility) is one of them. But none of the existing traffic simulators integrates models of EV that allow, for example, perform simulation studies regarding energy consumption. SUMO is a portable open source simulator with multi-modal traffic feature capabilities that permit the simulation of various types of vehicles. This work is an extension of the SUMO, two-dimensional (2D) vehicular simulation package. To allow the simulation of energy consumption of EV, two extensions were incorporated in SUMO: EV models and modeling of altitude, transforming SUMO into a three-dimensional (3D) simulator. The energy model effectiveness and correctness with 3D capabilities has been validated using two driving schedules (Urban Dynamometer Driving Schedule and Highway Fuel Economy Driving Schedule). This new tool will also support the study of better routes choice in 3D environment with EV aiming minimum energy consumption.

Bayesian Demand Calibration for Dynamic Traffic Simulations

Abstract: We present an operational framework for the calibration of demand models for dynamic traffic simulations, where calibration refers to the estimation of a structurally predefined model's parameters from real data. Our focus is on disaggregate simulators that represent every traveler individually. We calibrate, also at an individual level, arbitrary choice dimensions within a Bayesian framework, where the analyst's prior knowledge is represented by the dynamic traffic simulator itself and the measurements are comprised of time-dependent traffic counts. The approach is equally applicable to an equilibrium-based planning model and to a telematics model of spontaneous and imperfectly informed drivers. It is based on consistent mathematical arguments, yet it is applicable in a purely simulation-based environment and, as our experimental results show, is capable of handling large scenarios.

Interactive hybrid simulation of large-scale traffic

Abstract: We present a novel, real-time algorithm for modeling large-scale, realistic traffic using a hybrid model of both continuum and agent-based methods for traffic simulation. We simulate individual vehicles in regions of interest using state-of-the-art agent-based models of driver behavior, and use a faster continuum model of traffic flow in the remainder of the road network. Our key contributions are efficient techniques for the dynamic coupling of discrete vehicle simulation with the aggregated behavior of continuum techniques for traffic simulation. We demonstrate the flexibility and scalability of our interactive visual simulation technique on extensive road networks using both real-world traffic data and synthetic scenarios. These techniques demonstrate the applicability of hybrid techniques to the efficient simulation of large-scale flows with complex dynamics.

Performance measurement of a transportation network with a downtown space reservation system: A network-DEA approach☆

Abstract: We propose an evaluation approach for a novel travel demand management strategy known as the downtown space reservation system (DSRS). This approach takes into account three perspectives, i.e., transportation service provider’s, the user’s, and the community’s and is based on network-Data Envelopment Analysis (DEA) where the perspectives are inter-related through intermediate inputs/outputs. Two types of network-DEA models (radial and slacks-based models) are considered. An example is provided using data propagated from a microscopic traffic simulation model of the DSRS. The results show that individual node performance can drive network DEA performance and that this information can inform future designs of the DSRS.

Optimizing Minimum and Maximum Green Time Settings for Traffic Actuated Control at Isolated Intersections

Abstract: Optimization of signal control at isolated intersections has been an important research focus in traffic engineering over the past few years. Due to its flexibility and practicality, fully actuated control has been extensively deployed. In the conventional actuated control scheme, two important parameters, i.e., minimum and maximum green times, are arbitrarily prespecified, although it is widely recognized that they can significantly impact system operations. Previous studies have concentrated on computing these parameters using deterministic models. Due to the stochastic features of traffic arrival, such statically designated green time boundaries cannot sufficiently handle various traffic demands. To solve this problem, a stochastic model is established to dynamically optimize the minimum and maximum green times using real-time queue lengths and traffic arrival characteristics for each phase. Multiple criteria are fused and exploited as control objectives, such as avoiding cycle failures, minimizing control delays, and maximizing total traffic throughputs. Performance of the proposed algorithms is examined using a microscopic traffic simulation program, i.e., VISSIM 4.30, under various scenarios. The results show that the control system operated by the proposed algorithm produces promising improvements in system operation efficiency and fairness under various traffic demands.

Heuristic Algorithm for Priority Traffic Signal Control

Abstract: A heuristic algorithm is presented for traffic signal control with simultaneous multiple priority requests at isolated intersections in the context of vehicle-to-infrastructure communications being available on priority vehicles, such as emergency vehicles and transit buses. This heuristic algorithm can achieve near-optimal signal timing when all simultaneous requests are considered and can be visualized in a phase-time diagram. First, the problem with the control of multiple priority traffic signals is transformed into a network cut problem that is polynomial solvable under some reasonable assumptions. Second, a phase-time diagram is presented to visualize and evaluate priority delay given a signal plan and a collection of priority request arrival times. Microscopic traffic simulation is used to compare the heuristic with the state-of-the-practice algorithms for transit signal priority. The proposed heuristic algorithm could reduce average bus delay in congested conditions by about 50%, especially with a...

Travel Time Prediction Using Floating Car Data Applied to Logistics Planning

Abstract: Travel time information plays an important role in transportation and logistics. Much research has been done in the field of travel time prediction in local areas, aiming at accurate short-term predictions based on the current traffic situation and historical data of the area. In contrast, literature on prediction methods for long-range trips in large areas is rare, although it is highly relevant for logistics companies to manage their fleet of vehicles. In this paper, we present a new algorithm for predicting the remaining travel times of long-range trips. It makes use of nonparametric distribution-free regression models, which are applicable only in the presence of a sufficiently large database. Since, in contrast to local areas, such a base is visionary for large areas, we bring into play a dynamic data preparation to artificially enlarge the database. The algorithm also takes into account that routes of long-range trips are not completely given in advance but are rather unknown and subject to change. We illustrate our algorithm by means of simulations and a real-life case study at a German logistics company. The latter shows that, by our algorithm, the average relative error can be halved compared with conventional methods.

Agent-based traffic simulation and traffic signal timing optimization with GPU

Abstract: With the advantage of simulating the details of a transportation system, the “microsimulation” of a traffic system has long been a hot topic in the Intelligent Transportation Systems (ITS) research The Cellular Automata (CA) and the Multi-Agent System (MAS) modeling are two typical methods for the traffic microsimulation However, the computing burden for the microsimulation and the optimization based on it is usually very heavy In recent years the Graphics Processing Units (GPUs) have been applied successfully in many areas for parallel computing Compared with the traditional CPU cluster, GPU has an obvious advantage of low cost of hardware and electricity consumption In this paper we build an MAS model for a road network of four signalized intersections and we use a Genetic Algorithm (GA) to optimize the traffic signal timing with the objective of maximizing the number of the vehicles leaving the network in a given period of time Both the simulation and the optimization are accelerated by GPU and a speedup by a factor of 195 is obtained In the future we will extend the work to large scale road networks

Simulation-based analysis of personal rapid transit systems: service and energy performance assessment of the Masdar City PRT case

Abstract: This article, from a special issue on the modeling and optimization of transportation systems, introduces a simulation tool, miPRT, that can be used for designing and evaluating personal rapid transit (PRT) system applications. The authors apply their model to Masdar City, Abu Dhabi, United Arab Emirates (UAE). Masdar City is a zero-emission model city and is implementing a fully automated on-demand PRT system for its intracity transportation needs. The car-sized electric vehicles will run on an underground road network transporting passengers and freight throughout the city. Freight rapid transit (FRT) means that all deliveries to hotels, retail outlets, offices, and residencies need to be planned and scheduled through certified logistics providers, because conventional trucks are not allowed to enter the city. Through use of the miPRT simulation tool, the authors estimate the impact of different vehicle allocation algorithms, battery charging strategies, and vehicle occupancy rates. The model also anticipates the system's behavior under stress loads in order to rate its capacity limitations under travel demand surges (due to special events as well as to track close-down scenarios). The authors found that additional strategies would need to be implemented during these times of high demand and that separating PRT and FRT functions and stations contributes to a smoother sharing of the same infrastructure. They conclude that their simulation model assists in improving fleet utilization, energy consumption, and overall system costs.

Integration of Driving and Traffic Simulation: Issues and First Solutions

Abstract: Driving simulators are very suitable test beds for the evaluation and development of intelligent transportation systems (ITSs). However, the impact of such systems on the behavior of individual drivers can properly be analyzed through driving simulators only if autonomous vehicles in the driving scenario move according to the system under evaluation. This condition means that the simulation of the traffic surrounding the interactive vehicle should already take into account the driver's behavior as affected by the system under analysis. Currently, this “loop” is not properly tackled, because the effects on individuals and traffic are, in general, separately and, often, independently evaluated. The integration of traffic and driving simulations, instead, may provide a more consistent solution to this challenging evaluation problem. It also opens up new scenarios for enhancing the credibility of both traffic modeling and driving simulation and for their combined development. For instance, because drivers directly interact with driver/traffic models in a driving simulation environment, such models may also be tested against nonnormative behavior, and this case seems the only way to test driver/traffic models for safety applications. Based on this idea, this paper describes the integration of a driving simulation engine known as SCANeR and a traffic-flow microsimulation model known as AIMSUN. Methodological and technical issues of such integration are first presented, and future enhancements for higher consistency of the simulation environments are finally envisaged.

Towards reducing traffic congestion using cooperative adaptive cruise control on a freeway with a ramp

Abstract: Purpose: In this paper, the impact of Cooperative Adaptive Cruise Control (CACC) systems on traffic performance is examined using microscopic agent-based simulation. Using a developed traffic simulation model of a freeway with an on-ramp - created to induce perturbations and to trigger stop-and-go traffic, the CACC system’s effect on the traffic performance is studied. The previously proposed traffic simulation model is extended and validated. By embedding CACC vehicles in different penetration levels, the results show significance and indicate the potential of CACC systems to improve traffic characteristics and therefore can be used to reduce traffic congestion. The study shows that the impact of CACC is positive but is highly dependent on the CACC market penetration. The flow rate of the traffic using CACC is proportional to the market penetration rate of CACC equipped vehicles and the density of the traffic. Design/methodology/approach: This paper uses microscopic simulation experiments followed by a quantitative statistical analysis. Simulation enables researchers manipulating the system variables to straightforwardly predict the outcome on the overall system, giving researchers the unique opportunity to interfere and make improvements to performance. Thus with simulation, changes to variables that might require excessive time, or be unfeasible to carry on real systems, are often completed within seconds. Findings: The findings of this paper are summarized as follow: • Provide and validate a platform (agent-based microscopic traffic simulator) in which any CACC algorithm (current or future) may be evaluated. • Provide detailed analysis associated with implementation of CACC vehicles on freeways. • Investigate whether embedding CACC vehicles on freeways has a significant positive impact or not. Research limitations/implications: The main limitation of this research is that it has been conducted solely in a computer laboratory. Laboratory experiments and/or simulations provide a controlled setting, well suited for preliminary testing and calibrating of the input variables. However, laboratory testing is by no means sufficient for the entire methodology validation. It must be complemented by fundamental field testing. As far as the simulation model limitations, accidents, weather conditions, and obstacles in the roads were not taken into consideration. Failures in the operation of the sensors and communication of CACC design equipment were also not considered. Additionally, the special HOV lanes were limited to manual vehicles and CACC vehicles. Emergency vehicles, buses, motorcycles, and other type of vehicles were not considered in this dissertation. Finally, it is worthy to note that the human factor is far more sophisticated, hard to predict, and flexible to be exactly modeled in a traffic simulation model perfectly. Some human behavior could occur in real life that the simulation model proposed would fail to model. Practical implications: A high percentage of CACC market penetration is not occurring in the near future. Thus, reaching a high penetration will always be a challenge for this type of research. The public accessibility for such a technology will always be a major practical challenge. With such a small headway safety gap, even if the technology was practically proven to be efficient and safe, having the public to accept it and feel comfortable in using it will always be a challenge facing the success of the CACC technology. Originality/value: The literature on the impact of CACC on traffic dynamics is limited. In addition, no previous work has proposed an open-source microscopic traffic simulator where different CACC algorithms could be easily used and tested. We believe that the proposed model is more realistic than other traffic models, and is one of the very first models to model the behavior CACC vehicles on freeways.

Vehicle-type dependent car-following model for heterogeneous traffic conditions

Abstract: Car-following behavior forms the kernel of traffic microsimulation models and is extensively studied for similar vehicle types. However, in heterogeneous traffic having a diverse mix of vehicles, following behavior also depends on the type of both the leader and following vehicles. This paper is an attempt to modify the widely used Gipps’s car-following model to incorporate vehicle-type dependent parameters. Performance of the model is studied at microscopic and macroscopic levels using data collected from both homogeneous and heterogeneous traffic conditions. The results indicate that the proposed modifications enhance the prediction of follower behavior and suggest the need of incorporating vehicle-type combination specific parameters into traffic simulation models.

Virtual Testbed for Assessing Probe Vehicle Data in IntelliDrive Systems

Abstract: This paper presents an effort to develop a virtual testbed for assessing probe vehicle data generation by IntelliDrive vehicles within a microscopic traffic-simulation environment. Simulation capabilities are implemented through the development of a portable plug-in module using the application programming interface of the Paramics microscopic traffic simulation. This module simulates the generation of snapshots by individual vehicles, the uploading of these snapshots to roadside units, and some probe vehicle data postprocessing. While some temporary simplifying assumptions are made, the simulation generally follows operational concepts described in the Society of Automotive Engineers (SAE) J2735 Surface Vehicle Standard. Application of the model is demonstrated by simulating IntelliDrive probe data collection over the U.S. Department of Transportation (USDOT)'s Michigan Proof-of-Concept testbed. Simulation results show the sensitivity of probe data collection to communication range, market penetration, number of active roadside communication units (RSEs), interval between snapshots, and snapshot buffer size. Impacts on link travel time estimates are also presented. These results clearly demonstrate the utility of the simulator in conducting evaluations and sensitivity analyses for scenarios that would be difficult to execute in existing testbeds.

Platoon-based traffic flow model for estimating breakdown probability at single-lane expressway bottlenecks

Abstract: This study investigates the mechanism of traffic breakdown and establishes a traffic flow model that precisely simulates the stochastic and dynamic processes of traffic flow at a bottleneck. The proposed model contains two models of stochastic processes associated with traffic flow dynamics: a model of platoon formation behind a bottleneck and a model of speed transitions within a platoon. After these proposed models are validated, they are applied to a simple one-way, one-lane expressway section containing a bottleneck, and the stochastic nature of traffic breakdown is demonstrated through theoretical exercises.

Towards the next-generation traffic simulation tools: a first appraisal

Abstract: Future Urban Transport (FUT) describes all desired features that are currently being envisaged within the umbrella of Intelligent Transportation Systems. With advances in computer and communication technology, elevating thus the user to a central concern rather than favoring performance only, both the scientific community and practitioners are in search for adequate ways to model and assess new performance measures brought about by FUT's requirements. After identifying such requirements, we'll try to propose taxonomy on the basis of diverse criteria to assess how suitable currently available simulation packages are to assess Future Urban Transport. Some tools are compared and their ability to suit these needs is discussed, resulting in a first appraisal of suitability of existing microscopic traffic simulation tools. On the basis of the agent metaphor and the concept of multi-agent systems, we suggest ways in which to follow up this work.

Interactive hybrid simulation of large-scale traffic

Abstract: We present a novel, real-time algorithm for modeling large-scale, realistic traffic using a hybrid model of both continuum and agent-based methods for traffic simulation. We simulate individual veh...

A Game-Engine-Based Platform for Modeling and Computing Artificial Transportation Systems

Abstract: A game-engine-based modeling and computing platform for artificial transportation systems (ATSs) is introduced. As an important feature, the artificial-population module (APM) is described in both its macroscopic and microcosmic aspects. In this module, each person is designed similarly to the actors in games. The traffic-simulation module (TSM) is another important module, which takes advantage of Delta3D to construct a 3-D simulation environment. All mobile actors are also managed by this module with the help of the dynamic-actor-layer (DAL) mechanism that is offered by Delta3D. The platform is designed as agent-oriented, modularized, and distributed. Both modules, together with components that are responsible for message processing, rules, network, and interactions, are organized by the game manager (GM) in a flexible architecture. With the help of the network component, the platform can be constructed to implement a distributed simulation. Finally, four experiments are introduced to show functions and features of the platform.

Multiregime Sequential Risk-Taking Model of Car-Following Behavior: Specification, Calibration, and Sensitivity Analysis

Abstract: Car-following models constitute the main component of operational microscopic simulation models and are intended to capture intervehicle interactions on highway sections. Most existing car-following models are deterministic and do not capture the effects of surrounding traffic conditions on the decision-making process of the driver. An extension to a previously introduced sequential risk-taking model is offered to capture the effects of surrounding conditions on driving behavior. The model extension recognizes two behavioral regimes that depend on the complexity of the decision situation associated with the prevailing congestion. With each regime is associated a value function capturing driver preferences for gains associated with a particular acceleration. A probabilistic regime selection mechanism relates the driver's choices to prevailing traffic conditions. The model is calibrated against actual trajectory data. Initial results show that the model provides realistic behavioral patterns previously iden...

Dynamic urban road network traffic zone partitioning method based on space cluster analysis

Abstract: A dynamic urban road network traffic zone partitioning method estimates the traffic state parameters of the road network according to the exist taxi GPS data, considers the OD characteristic of urban traffic and adopts road section congestion degree and road section degree of association for space statistical analysis, and accordingly analyzes the space autocorrelation mode among road sections torealize the automatic partitioning of the traffic zone The invention has the beneficial effects that: according to the traffic simulation inspection results: when the rate of coverage of taxis in the road network volume of traffic reaches 5%, the estimation accuracy of the road network traffic state is more than 80%; according to the estimation accuracy, the vehicles are guided to avoid the traffic congestion area, and necessary control management strategies are adopted to reduce the scale of the traffic congestion area and congestion time and accelerate the traffic congestion dissipation The method and technology adopted in the invention are simple, feasible and have easily-met operation conditions, and are easy for popularization and application in nationwide large and medium-sized cities

Multimodal Transport in Nairobi, Kenya: Insights and Recommendations with a Macroscopic Evidence-Based Model

Abstract: The limited street network in Nairobi, Kenya, is crowded with cars and matatus (informal transit). This paper studies the existing traffic performance using traffic simulation. Despite the poorly connected, asymmetric street network, there is a consistent relationship between aggregated traffic variables for the city center, namely the number of vehicles circulating in the network and the rate at which trips reach their destinations. This relation is called a macroscopic fundamental diagram (MFD), and shows how the capacity of Nairobi's streets compares to the capacity of streets in other cities. The MFD also shows how traffic delays will increase with continued growth in demand. This method of looking at traffic performance is also used to identify policies which will improve traffic conditions in Nairobi. These policies include metering the rate that vehicles enter the city, spreading peak demand, improving intersection operations, dedicating lanes to buses and matatus, and strategically adding redundant links to the network. If nothing is done, traffic conditions in Nairobi will get worse, but by monitoring traffic and adopting some cost-effective policies, the capacity of the network to serve trips can be substantially improved.