Combined Model for Signal Control and Route Choice in Urban Traffic Networks

1996 ◽  
Vol 1554 (1) ◽  
pp. 27-35 ◽  
Author(s):  
Nathan H. Gartner ◽  
Mohammed Al-Malik
Keyword(s):  
Urban Areas ◽  
Traffic Management ◽  
Network Performance ◽  
Travel Demand ◽  
Management Strategies ◽  
Fixed Time ◽  
Urban Traffic ◽  
Traffic Flows ◽  
Traffic Volumes ◽  
Computational Procedures

Traffic signals have a significant effect on the choice of routes by motorists in urban areas. They are of primary importance in the development of advanced traffic management strategies that involve dynamic rerouting of traffic flows through signal-controlled street networks. A combined network model that simultaneously accounts for both the route choices made by motorists and the desired signal controls to match these choices is presented. Given origin-destination travel demand information, the model generates signal controls to optimize network performance and calculates the resulting traffic volumes in the network. This optimization model inherently reflects the mutual consistency between traffic flows and signal controls. The model is applicable to both fixed-time and demand-responsive signals. Computational procedures and sample network solutions are presented.

Microscopic Simulation of Urban Traffic Based on Cellular Automata

1997 ◽  
Vol 08 (05) ◽  
pp. 1025-1036 ◽  
Author(s):  
J. Esser ◽  
M. Schreckenberg
Keyword(s):  
Cellular Automata ◽  
Traffic Control ◽  
Urban Areas ◽  
Traffic Management ◽  
Management Strategies ◽  
Urban Traffic ◽  
Large Network ◽  
Microscopic Simulation ◽  
Real Time Traffic ◽  
Traffic Systems

Saturated capacities in traffic systems evoke increasing interest in simulations of complex networks serving as laboratory environment for developing management strategies. Especially for urban areas questions concerning overall traffic control have to be considered with regard to their impacts on the whole network. Modeling traffic flow dynamics using cellular automata allows us to run large network traffic simulations with only comparatively low computational efforts. We present a traffic simulation tool for urban road networks which is based on the Nagel–Schreckenberg Model. Arbitrary kinds of roads and crossings are modeled as combinations of only a few basic elements. Furthermore parking capacities are considered as well as circulations of public transports. The vehicles are driven corresponding to route plans or at random depending on the available data. The application of this network simulation covers investigations on the field of traffic planning as well as online simulations based on real-time traffic data as basis for dynamic traffic management systems.

Traffic Management Parameters from Single Inductive Loop Detectors

2000 ◽  
Vol 1719 (1) ◽  
pp. 112-120 ◽  
Author(s):  
Tom Cherrett ◽  
Hugh Bell ◽  
Mike McDonald
Keyword(s):  
Traffic Control ◽  
Traffic Management ◽  
Fixed Time ◽  
Urban Traffic ◽  
Flow Conditions ◽  
Inductive Loop ◽  
Loop Detectors ◽  
Time Period ◽  
S Period ◽  
Urban Traffic Control

Investigated are potential new uses for the digital output produced by single inductive loop detectors (2 m x 1.5 m and 2 m x 6.5 m) used in most European urban traffic control systems. Over a fixed time period, the average loop-occupancy time per vehicle (ALOTPV) for a detector being sampled every 250 ms is determined by taking the number of 250-ms occupancies and dividing by the number of vehicles. In a similar way, the average headway time between vehicles (AHTBV) is determined by taking the number of 250-ms vacancies and dividing by the number of vehicles. Over a 30-s period, the minimum and maximum values of ALOTPV and AHTBV ranged from 1 to 120 (an ALOTPV of 1 and an AHTBV of 120 representing free-flow conditions, an ALOTPV of 120 and an AHTBV of 1 representing a stationary queue). Identifying periods when a link was operating under capacity and at capacity and when it had become saturated could be more clearly identified by using plots of ALOTPV and AHTBV data over time compared to the more traditional percentage occupancy output. ALOTPV also was used to successfully identify long vehicles from cars down to speeds of 15 km/h.

Traffic management in cities

2019 ◽  
Author(s):  
Елена Андреева ◽  
Elena Andreeva ◽  
Кристиан Бёттгер ◽  
Kristian Bettger ◽  
Екатерина Белкова ◽  
...  
Keyword(s):  
Traffic Management ◽  
Engineering Students ◽  
Urban Traffic ◽  
Urban Transport ◽  
Population Increase ◽  
Transport Systems ◽  
Traffic Flows ◽  
Organization And Management ◽  
Wide Range ◽  
Traffic Management System

The monograph is devoted to the consideration of issues relevant to the vast majority of cities-the organization and management of traffic flows to improve the mobility of the population, increase the speed and reduce the cost of transportation of passengers and goods, reduce the burden on the environment, etc. The book provides an overview of existing models, methods and tools for modeling and managing traffic flows in cities. The author identifies the main modern challenges to sustainable development of urban transport systems, which should be taken into account in the development of urban traffic management system. The authors substantiate the need for a systematic approach in the development of traffic management systems in cities and propose a practical tool for its implementation — an integrated digital platform for urban traffic management. Describes the experience of creation and application of an integrated automated control system of traffic management TransInfo and its improved version RITM, for the city of Moscow. In conclusion, the forecast of further development of research and development in the field of modeling and management of transport mobility is given. The book is of interest to a wide range of readers involved in the modeling and management of traffic flows, experts in the field of transport planning, scientists, engineers, economists and mathematicians, as well as graduate students and engineering students.

scholarly journals Dynamic Analysis of Traffic State and Congestion Propagation on Bidirectional Grid Network

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Shu-bin Li ◽  
Bai-bai Fu ◽  
Jian-feng Zheng
Keyword(s):  
Traffic Congestion ◽  
Traffic Management ◽  
Network Performance ◽  
Simulation Software ◽  
Urban Traffic ◽  
Grid Network ◽  
Traffic State ◽  
Proposed Model ◽  
Air Pollutions ◽  
Density Relationship

Many traffic problems in China such as traffic jams and air pollutions are mainly caused by the increasing traffic volume. In order to alleviate the traffic congestion and improve the network performance, the analysis of traffic state and congestion propagation has attracted a great interest. In this paper, an improved mesoscopic traffic flow model is proposed to capture the speed-density relationship on segments, the length of queue, the flow on links, and so forth, The self-developed dynamic traffic simulation software (DynaCHINA) is used to reproduce the traffic congestion and propagation in a bidirectional grid network for different demand levels. The simulation results show that the proposed model and method are capable of capturing the real traffic states. Hence, our results can provide decision supports for the urban traffic management and planning.

Microscopic Simulation on High-Class Roads: Enhancement of Environmental Analyses and Driving Dynamics: Practical Applications

2000 ◽  
Vol 1706 (1) ◽  
pp. 73-81 ◽  
Author(s):  
Ville Lehmuskoski ◽  
Jarkko Niittymäki ◽  
Björn Silfverberg
Keyword(s):  
Urban Areas ◽  
Management Strategies ◽  
Project Planning ◽  
Simulation Software ◽  
Urban Traffic ◽  
Detailed Calculation ◽  
Microscopic Simulation ◽  
Emission Analysis ◽  
Change Models ◽  
Practical Applications

Traffic is a very complex phenomenon. As the calculation power and capacity of computers have rapidly increased, the possibilities for modeling traffic corresponding better to reality have also increased. A noteworthy method for modeling is microscopic simulation of traffic, which can be used for a variety of needs. In general it can be stated that the interest in microsimulation lies in short-term functional studies rather than in the assessment of long-term needs. The design of concepts for traffic management strategies, especially for main transport corridors and urban traffic systems, constitutes a challenge for the user of microscopic simulation tools. In addition to practical project planning, microscopic traffic simulation can also provide a tool for research. Detailed calculation of emissions is of special interest in congested urban areas. Microscopic simulation of traffic provides comprehensive methods for detailed assessment of emissions as each individual vehicle is recorded with respect to its driving conditions and performance both in space and in time (speed, acceleration, deceleration). HUTSIM is a Finnish microscopic simulation software developed by the Helsinki University of Technology. Because it is rule based and object oriented, the operational comprehension of the software can be expanded quite easily. In many Finnish projects, HUTSIM has been expanded to better suit wide-scale applications. Development of emission analysis and lane-change models is described, as are future development outlines concerning modeling human behavior and analyzing the level of service using fuzzy methods.

scholarly journals OPTIMIZATION OF MANAGEMENT OF URBAN LIGHTS WITH THE USE OF NEURAL NETWORKS

THE BULLETIN ◽  
2021 ◽  
Vol 389 (1) ◽  
pp. 14-17
Author(s):  
A.А. Suleimen ◽  
G.B. Kashaganova ◽  
G.B. Issayeva ◽  
B.R. Absatarova ◽  
M.C. Ibraev
Keyword(s):  
Neural Networks ◽  
Mathematical Model ◽  
Traffic Management ◽  
Urban Traffic ◽  
Control Parameters ◽  
Traffic Flows ◽  
Traffic Light ◽  
Traffic Lights ◽  
The Road ◽  
On The Road

One of the most pressing problems of large cities is the problem of traffic management of vehicles. The reason for this problem is an imperfect way to manage traffic flows. Traffic light regulation is of particular importance in traffic management. Most modern traffic light control systems operate at set time intervals and are not able to cope with the constantly changing situation on the road. A promising direction for solving this problem is to optimize the system using artificial neural networks. The advantage of neural networks is self-learning, which allows the system to adapt to the changing situation on the road. Despite numerous attempts, it has not yet been possible to obtain a high-quality mathematical model of urban traffic management. This model should determine the functional dependence of transport flow parameters on control parameters. Nowadays, traffic flows are regulated everywhere by means of traffic lights. If we can get a fairly accurate mathematical model of traffic flows, we can determine the optimal duration of the traffic signal phases to achieve the maximum capacity of the road network node. A fairly accurate mathematical model of traffic management that works in predictive mode will display an estimate of the optimal control parameters, as well as make correct decisions in emergency situations. Well-known mathematical models of road traffic take into account only the average values of traffic flows, and not the exact number of cars on each road section at a particular time.

scholarly journals Evaluation of Reinforcement Learning Traffic Signalling Strategies for Alternative Objectives: Implementation in the Network of Nicosia, Cyprus

2020 ◽  
Vol 21 (4) ◽  
pp. 295-302
Author(s):  
Haris Ballis ◽  
Loukas Dimitriou
Keyword(s):  
Reinforcement Learning ◽  
Environmental Sustainability ◽  
Urban Areas ◽  
Traffic Management ◽  
Large Scale ◽  
Smart Cities ◽  
Control Strategies ◽  
Urban Traffic ◽  
Signal Control ◽  
Urban Network

AbstractSmart Cities promise to their residents, quick journeys in a clean and sustainable environment. Despite, the benefits accrued by the introduction of traffic management solutions (e.g. improved travel times, maximisation of throughput, etc.), these solutions usually fall short on assessing the environmental impact around the implementation areas. However, environmental performance corresponds to a primary goal of contemporary mobility planning and therefore, solutions guaranteeing environmental sustainability are significant. This study presents an advanced Artificial Intelligence-based (AI) signal control framework, able to incorporate environmental considerations into the core of signal optimisation processes. More specifically, a highly flexible Reinforcement Learning (RL) algorithm has been developed towards the identification of efficient but-more importantly-environmentally friendly signal control strategies. The methodology is deployed on a large-scale micro-simulation environment able to realistically represent urban traffic conditions. Alternative signal control strategies are designed, applied, and evaluated against their achieved traffic efficiency and environmental footprint. Based on the results obtained from the application of the methodology on a core part of the road urban network of Nicosia, Cyprus the best strategy achieved a 4.8% increase of the network throughput, 17.7% decrease of the average queue length and a remarkable 34.2% decrease of delay while considerably reduced the CO emissions by 8.1%. The encouraging results showcase ability of RL-based traffic signal controlling to ensure improved air-quality conditions for the residents of dense urban areas.

Quantifying the Mobility Benefits of Express Lanes using Real-Time Traffic Data

2020 ◽  
Vol 2674 (11) ◽  
pp. 414-423
Author(s):  
Cecilia Kadeha ◽  
Priyanka Alluri ◽  
Thobias Sando
Keyword(s):  
Traffic Congestion ◽  
Urban Areas ◽  
Traffic Management ◽  
Management Strategies ◽  
Time Of Day ◽  
General Purpose ◽  
Performance Measure ◽  
Study Results ◽  
Transportation Agencies ◽  
Express Lanes

Traffic congestion is one of the major problems facing transportation agencies, especially in urban areas. Agencies are exploring ways to use the existing transportation infrastructure efficiently by deploying appropriate traffic management strategies. One of these strategies is the use of express lanes, which are expected to effectively mitigate congestion and increase the reliability of highway facilities. Express lanes are managed toll lanes, separated from general-purpose lanes within a freeway facility. The goal of this study was to quantify the mobility benefits of express lanes by comparing the performance of express lanes with that of their adjacent general-purpose lanes, and by assessing the performance of the general-purpose lanes when the express lanes were open versus when the express lanes were closed. The Buffer Index (BI), a travel time reliability measure, was selected as the performance measure. The analysis was based on 95Express, express lanes along I-95 in Miami, Florida. Overall, the results indicated that BIs for the express lanes were significantly lower than the BIs for the general-purpose lanes, and the BIs for the general-purpose lanes were significantly lower when the express lanes were open compared with the periods when the express lanes were closed. The study results showed mobility improvements on both the express lanes and the general-purpose lanes, although the extent of the improvements varied by direction (i.e., northbound and southbound) and time of day (i.e., a.m. peak, p.m. peak, daytime off-peak, and nighttime off-peak). Transportation agencies may use these findings to quantify and evaluate the mobility benefits of the express lanes and the general-purpose lanes on express lane facilities.

scholarly journals Long-term urban traffic monitoring based on wireless multi-sensor network

2020 ◽  
Vol 10 (1) ◽  
pp. 197-208
Author(s):  
Wiktoria Loga-Księska ◽  
Justyna Sordyl ◽  
Artur Ryguła
Keyword(s):  
Sensor Network ◽  
Urban Areas ◽  
Traffic Management ◽  
Traffic Monitoring ◽  
Traffic Volume ◽  
Urban Traffic ◽  
Transport Systems ◽  
City Centre ◽  
The City

AbstractIncreasing the number of vehicles on the road network and the growing popularity of sustainable development of urban areas have resulted in the need for implementing efficient and cost-effective traffic measurement methods. From the perspective of traffic management, up-to-date information about vehicle density and access to historical data are the key components of traffic variability analyses. Rapid technological development based on Intelligent Transport Systems (ITS) has popularised the wireless sensor networks (WSN) application. The solution enables continuous monitoring of selected area using multiple wireless and low-cost sensors connected within a network. Those systems are dynamically evolving tools for solving an effective traffic management issues in city centres and urban environments. In the study, authors have performed a traffic variability and its dynamics analysis in a selected area using a multi-sensor network for traffic volume monitoring. The article presents the results of research conducted between years 2015 - 2018 throughout the city of Bielsko-Biala with the support of OnDynamic multimodal system. Within the context of the analyses, basic traffic parameters have been determined and variability trends have been identified on selected road sections. Long-term research indicated the minor variation in a number of vehicle detections and relatively stable traffic volume in the city centre during the analysis period.

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