Self-organization models of urban traffic lights based on digital infochemicals

SIMULATION ◽  
2018 ◽  
Vol 95 (3) ◽  
pp. 271-285 ◽  
Author(s):  
Guangyu Zou ◽  
Levent Yilmaz
Keyword(s):  
Traffic Congestion ◽  
Urban Areas ◽  
Urban Transportation ◽  
Urban Traffic ◽  
Self Organization ◽  
Traffic Lights ◽  
Traffic Systems ◽  
Signaling Strategies ◽  
Better Than ◽  
Organization Models

This paper presents a self-organizing model to design effective traffic signaling strategies in order to reduce traffic congestion in urban areas. The proposed traffic signaling system is based on a pattern model of self-organization, i.e., digital infochemicals (DIs), which are analogous to chemical substances that convey information between interactive elements mediated via the environment. In the context of traffic systems, the DIs refer to information generated by vehicles and dissipated by the urban transportation infrastructure. Based on the exploratory analysis with one single intersection, we demonstrate that the DI-based strategy performs significantly better than both the fixed and trigger-based scheduling strategies in terms of queue length and waiting time under both fixed and dynamic traffic demands.

Research on the Urban Transportation Development in Our Country Based on the Traffic Congestion Pricing

2011 ◽  
Vol 97-98 ◽  
pp. 1032-1037
Author(s):  
Wei Kou ◽  
Lin Cheng
Keyword(s):  
Traffic Congestion ◽  
Marginal Cost ◽  
Demand Management ◽  
Urban Transportation ◽  
Congestion Pricing ◽  
Developed Countries ◽  
Urban Traffic ◽  
Theory And Practice ◽  
Traffic Demand ◽  
Transportation Development

With the development and realization of industrialization and urbanization in the world, urban traffic volume grows rapidly; many big cities face more and more serious traffic problem. As a mean of traffic demand management, traffic congestion pricing has important significance in theory and practice. Traffic congestion pricing can counteract external diseconomy caused by network congestion, and the price of congestion is tantamount to the difference between social marginal cost and private marginal cost. This paper analyzes the economic theory of congestion pricing. Combined the effect of traffic congestion pricing that implemented in the developed countries, it researches the influence of urban transportation development in our country in the future based on the implementing congestion pricing.

Measuring Economic Costs of Urban Traffic Congestion to Business

2003 ◽  
Vol 1839 (1) ◽  
pp. 98-106 ◽  
Author(s):  
Glen Weisbrod ◽  
Don Vary ◽  
George Treyz
Keyword(s):  
Traffic Congestion ◽  
Urban Areas ◽  
Economic Benefits ◽  
Urban Traffic ◽  
Production Costs ◽  
Market Area ◽  
Economic Costs ◽  
Local Data ◽  
Economic Implications ◽  
Reduction Strategies

Key findings are provided from NCHRP Study 2-21, which examined how urban traffic congestion imposes economic costs within metropolitan areas. Specifically, the study applied data from Chicago and Philadelphia to examine how various producers of economic goods and services are sensitive to congestion, through its impact on business costs, productivity, and output levels. The data analysis showed that sensitivity to traffic congestion varies by industry sector and is attributable to differences in each industry sector's mix of required inputs and hence its reliance on access to skilled labor, access to specialized inputs, and access to a large, transportation-based market area. Statistical analysis models were applied with the local data to demonstrate how congestion effectively shrinks business market areas and reduces the "agglomeration economies" of businesses operating in large urban areas, thus raising production costs. Overall, this research illustrates how it is possible to estimate the economic implications of congestion, an approach that may be applied in the future for benefit-cost analysis of urban congestion-reduction strategies or for development of congestion pricing strategies. The analysis also shows how congestion-reduction strategies can induce additional traffic as a result of economic benefits.

Study on Park and Ride Locating Based on Transportation Demand Management

2014 ◽  
Vol 1030-1032 ◽  
pp. 2254-2259
Author(s):  
Jin De Cai ◽  
Ke Zhang
Keyword(s):  
Traffic Congestion ◽  
Transportation Network ◽  
Demand Management ◽  
Urban Transportation ◽  
Urban Traffic ◽  
Transportation Costs ◽  
Construction Costs ◽  
Transportation Management ◽  
Transportation Demand ◽  
Park And Ride

With the increasingly serious problem of urban traffic congestion, more attention is focused on the Park and Ride (P&R) schemes based on urban transportation demand (TDM) management. The P&R locating research, as an important part of the scheme, plays an important role to strengthen the transportation management. On the basis of identifying all the potential P&R locations, and from the macroscopic perspective of urban transportation network, this paper establishes a model of P&R locating in order to minimize their construction costs as well as the total transportation costs. Example analysis is finally carried out with the help of Lingo software, thus testifying the validity of this research.

Analysis of Trends in Data on Transit Bus Dwell Times

2017 ◽  
Vol 2619 (1) ◽  
pp. 64-74 ◽  
Author(s):  
Isaac K. Isukapati ◽  
Hana Rudová ◽  
Gregory J. Barlow ◽  
Stephen F. Smith
Keyword(s):  
Traffic Congestion ◽  
Urban Areas ◽  
Traffic Signal ◽  
Urban Traffic ◽  
Distribution Model ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Real Time Control ◽  
Arrival Times ◽  
On The Road

Transit vehicles create special challenges for urban traffic signal control. Signal timing plans are typically designed for the flow of passenger vehicles, but transit vehicles—with frequent stops and uncertain dwell times—may have different flow patterns that fail to match those plans. Transit vehicles stopping on urban streets can also restrict or block other traffic on the road. This situation results in increased overall wait times and delays throughout the system for transit vehicles and other traffic. Transit signal priority (TSP) systems are often used to mitigate some of these issues, primarily by addressing delay to the transit vehicles. However, existing TSP strategies give unconditional priority to transit vehicles, exacerbating quality of service for other modes. In networks for which transit vehicles have significant effects on traffic congestion, particularly urban areas, the use of more-realistic models of transit behavior in adaptive traffic signal control could reduce delay for all modes. Estimating the arrival time of a transit vehicle at an intersection requires an accurate model of dwell times at transit stops. As a first step toward developing a model for predicting bus arrival times, this paper analyzes trends in automatic vehicle location data collected over 2 years and allows several inferences to be drawn about the statistical nature of dwell times, particularly for use in real-time control and TSP. On the basis of this trend analysis, the authors argue that an effective predictive dwell time distribution model must treat independent variables as random or stochastic regressors.

scholarly journals Understanding the Factors That Are Associated with Motorcycle Crash Severity in Rural and Urban Areas of Ghana

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
William Agyemang ◽  
Emmanuel Kofi Adanu ◽  
Steven Jones
Keyword(s):  
Rural Areas ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Urban Traffic ◽  
Sub Saharan Africa ◽  
Crash Data ◽  
Rural And Urban Areas ◽  
Rural And Urban ◽  
Run Off ◽  
Sub Saharan

Like many countries in sub-Saharan Africa, Ghana has witnessed an increase in the use of motorcycles for both commercial transport and private transport of people and goods. The rapid rise in commercial motorcycle activities has been attributed to the problem of urban traffic congestion and the general lack of reliable and affordable public transport in rural areas. This study investigates and compares factors that are associated with motorcycle crash injury outcomes in rural and urban areas of Ghana. This comparison is particularly important because the commercial use of motorcycles and their rapid growth in urban areas are a new phenomenon, in contrast to rural areas where people have long relied on motorcycles for their transportation needs. Preliminary analysis of the crash data revealed that more of the rural area crashes occurred under dark and unlit roadway conditions, while urban areas recorded more intersection-related crashes. Additionally, it was found that more pedestrian collisions happened in urban areas, while head-on collisions happened more in rural areas. The model estimation results show that collisions with a pedestrian, run-off-road, and collisions that occur under dark and unlit roadway conditions were more likely to result in fatal injury. Findings from this study are expected to help in crafting and targeting appropriate countermeasures to effectively reduce the occurrence and severity of motorcycle crashes throughout the country and, indeed, sub-Saharan Africa.

Smart Cities: Study and Comparison of Traffic Light Optimization in Modern Urban Areas Using Artificial Intelligence

2018 ◽  
Vol 8 (2) ◽  
pp. 10
Author(s):  
Mustapha Kabrane ◽  
Salah-ddine Krit ◽  
Lahoucine El Maimouni
Keyword(s):  
Traffic Congestion ◽  
Urban Areas ◽  
Road Network ◽  
Smart Cities ◽  
Optimization Techniques ◽  
Traffic Light ◽  
Large Cities ◽  
Traffic Lights ◽  
The Road ◽  
Road Signs

In large cities, the increasing number of vehicles private, society, merchandise, and public transport, has led to traffic congestion. Users spend much of their time in endless traffic congestion. To solve this problem, several solutions can be envisaged. The interest is focused on the  system of road signs: The use of a road infrastructure is controlled by a traffic light controller, so it is a matter of knowing how to make the best use of the controls of this system (traffic lights) so as to make traffic more fluid. The values of the commands computed by the controller are determined by an algorithm which is ultimately, only solves a mathematical model representing the problem to be solved. The objective is to make a study and then the comparison on the optimization techniques based on artificial intelligence1 to intelligently route vehicle traffic. These techniques make it possible to minimize a certain function expressing the congestion of the road network. It can be a function, the length of the queue at intersections, the average waiting time, also the total number of vehicles waiting at the intersection

TRANSPORTATION AND QUALITY OF LIFE

2020 ◽  
Vol 18 (13) ◽  
Author(s):  
Abdul Ghapar Othman ◽  
Kausar Hj. Ali
Keyword(s):  
Quality Of Life ◽  
Urban Area ◽  
Public Transportation ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Urban Transportation ◽  
Potential Threat ◽  
Private And Public ◽  
Negative Impacts

Transportation is one of the key indicators used to measure the Quality of Life of people especially those living in the urban area. Many aspects of transportation are very significant as they have the power to directly influence our way of life in search for a better Quality of Life. Many Malaysians depend on private vehicle transportations to cater their daily travel needs which inevitably leads to an over infiltration of vehicles into the urban area. Automobile dependency has always been viewed as a potential threat to Malaysia’s urban areas, as it contributes to the increase in traffic congestion, higher accidents rate, inefficient usage of urban land, environmental pollution, adverse economic impacts, urban sprawling and reduces the overall quality of public transportation. All these negative impacts deteriorate the quality of life of urban dwellers. This chapter will discuss Malaysia's urban transportation in general, focusing on the struggle between private and public transportation usage and the impacts of automobile dependency towards the urban dwellers’ Quality of Life; as well as putting forward possible strategies and measures in an attempt to provide a balanced urban transportation system.

scholarly journals Pay Your Trip for Traffic Congestion: Dynamic Pricing in Traffic-Aware Road Networks

2020 ◽  
Vol 34 (01) ◽  
pp. 582-589
Author(s):  
Lisi Chen ◽  
Shuo Shang ◽  
Bin Yao ◽  
Jing Li
Keyword(s):  
Dynamic Pricing ◽  
Traffic Congestion ◽  
Intelligent Transportation ◽  
Urban Traffic ◽  
Pricing Strategy ◽  
Computation Complexity ◽  
Matching Algorithm ◽  
Matching Efficiency ◽  
Traffic Systems ◽  
Pruning Techniques

Pricing is essential in optimizing transportation resource allocation. Congestion pricing is widely used to reduce urban traffic congestion. We propose and investigate a novel Dynamic Pricing Strategy (DPS) to price travelers' trips in intelligent transportation platforms (e.g., DiDi, Lyft, Uber). The trips are charged according to their “congestion contributions” to global urban traffic systems. The dynamic pricing strategy retrieves a matching between n travelers' trips and the potential travel routes (each trip has k potential routes) to minimize the global traffic congestion. We believe that DPS holds the potential to benefit society and the environment, such as reducing traffic congestion and enabling smarter and greener transportation. The DPS problem is challenging due to its high computation complexity (there exist kn matching possibilities). We develop an efficient and effective approximate matching algorithm based on local search, as well as pruning techniques to further enhance the matching efficiency. The accuracy and efficiency of the dynamic pricing strategy are verified by extensive experiments on real datasets.

Real-Time Monitoring Unit of Traffic Flow

2012 ◽  
Vol 588-589 ◽  
pp. 1058-1061
Author(s):  
Ting Zhang ◽  
Zhan Wei Song
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Traffic Congestion ◽  
Urban Traffic ◽  
Traffic Information ◽  
Real Time Monitoring ◽  
Traffic Lights ◽  
Real Time Traffic ◽  
Transit Use ◽  
Control Traffic

With the sustained growth of vehicle ownerships, traffic congestion has become obstacle of urban development. In addition to developing public transport and accelerating the construction of rail transit, use scientific managing and controlling method in real-time monitoring traffic flow to divert the traffic stream is an effective way to solve urban traffic problems. In this paper, cross-correlation algorithm is used to obtain real-time traffic information, such as capacity and occupancy of a lane, so as to control traffic lights intelligently.

scholarly journals Traffic Light Controller using Image Processing

2021 ◽  
Vol 12 (2) ◽  
pp. 405-411 ◽  
Author(s):  
Lakshmanan M, Et. al.
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Real Time ◽  
Traffic Congestion ◽  
Traffic Signals ◽  
Daily Basis ◽  
Traffic Density ◽  
Traffic Light ◽  
Traffic Lights ◽  
Traffic Systems

Traffic congestion at junctions is a serious issue on a daily basis. The prevailing traffic light controllers are unable to manage the different traffic flows. Most of the current systems operate on a timing mechanism that changes the signal after a particular interval of time. This may cause frustration and result in motorist's time waste. Traffic congestion is a major problem in the currently existing systems. Delays, safety, parking, and environmental problems are the main issues of current traffic systems that emit smoke and contribute to increasing Global Warming. Sensor-based systems reduce the waiting time and maximize the total number of vehicles that can cross an intersection. Our proposed system can control the traffic lights based on image processing without the need for traffic police. This can reduce congestion, delay, road accidents, need for manpower. Under image processing, we use sub techniques like RGB to Gray conversion, Image resizing, Image Enhancement, Edge detection, Image matching, and Timing allocation. A real-time image is captured for every 1 second. After edge detection procedure for both reference and real-time images, these images are compared using SURF Algorithm. Then the amount of traffic is detected and the details are stored in the server. Arduino is used for a traffic signal in the hardware part. It consists of a Wi-Fi module. The micro-controller used in the system Arduino. Four cameras are placed on respective roads and these cameras are used to capture images to analyze traffic density. Then the traffic signals are decided according to the density of traffic. Our technique can be effective to combat traffic on Indian Roads. A lot of time can be saved by deploying this system and also it conserves a lot of resources as well as the economy

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