Developing a Measure of Traffic Congestion: Fuzzy Inference Approach

2002 ◽  
Vol 1802 (1) ◽  
pp. 77-85 ◽  
Author(s):  
Khaled Hamad ◽  
Shinya Kikuchi
Keyword(s):  
Traffic Congestion ◽  
Urban Areas ◽  
Fuzzy Inference ◽  
Supply And Demand ◽  
Travel Speed ◽  
Flow Speed ◽  
Inference Process ◽  
Real World Data ◽  
Total Travel Time ◽  
The Individual

Many measures have been proposed to represent the status of traffic conditions on arterial roadways in urban areas. The debate about what is the most appropriate measure continues. In a contribution to the debate, another approach was offered. Traditionally, two general approaches exist. One is based on the relationship between supply and demand. The other is a measure relative to the most acceptable status of service quality. The latter measure allows the public to relate to their travel experience. In either case, however, derivation of measures of congestion involves uncertainty because of imprecision of the measurement, the traveler’s perception of acceptability, variation in sample data, and the analyst’s uncertainty about causal relations. A measure is proposed that is a composite of two traditional measures, travel speed and delay. In recognition of the uncertainty, a fuzzy inference process was proposed. The inputs are travel speed, free-flow speed, and the proportion of very low speed in the total travel time. These values were processed through fuzzyrule-based inference. The outcome was a single congestion index value between 0 and 1, where 0 is the best condition and 1 is the worst condition. The process was demonstrated using real-world data. The results were compared with those of the Highway Capacity Manual. Although no conclusion can be drawn about the best measure of congestion, the proposed inference process allows the mechanism to combine different measures and also to incorporate the uncertainty in the individual measures so that the composite picture of congestion can be reproduced.

scholarly journals A Novel Framework to Control and Optimize the Traffic Congestion Issue in VANET

2018 ◽  
Vol 7 (2.31) ◽  
pp. 245
Author(s):  
Tanuja Kayarga ◽  
H M. Navyashree
Keyword(s):  
Traffic Congestion ◽  
Urban Areas ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Fuzzy Inference ◽  
Vehicular Traffic ◽  
Vehicular Communication ◽  
Hoc Networks ◽  
Set Up ◽  
Inference Systems

In the recent times due to the increase of vehicular nodes in a vehicular communication network, there is a need of developing efficient systems in order to optimize the vehicular traffic congestion issues in urban areas. The current research trends shows that most of the conventional studies focused on developing fuzzy inference systems based vehicular traffic congestion model which has gained lots of attention on detecting and minimizing the congestion levels.We have proposed a new approach towards detection and controlling of traffic congestion in VANET. The proposed system utilizes the communication channels very efficiently and irrespective of any kind of overload. This proposed system aims to introduce a novel framework for identifying traffic jam on Vehicular Ad-hoc Networks. In order to detect and minimize the level of congestion our approach will use a fuzzy logic based approach to notify the drivers about available routes during the traffic congestion. An experimental prototype will be set up to enable the graphical simulation.

scholarly journals Floating Car Data and Fuzzy Logic for classifying congestion indexes in the city of Shanghai

2019 ◽  
Vol 2 ◽  
pp. 1-7
Author(s):  
Maja Kalinic ◽  
Jukka M. Krisp
Keyword(s):  
Fuzzy Logic ◽  
Traffic Congestion ◽  
Fuzzy Inference ◽  
Inference Model ◽  
Floating Car Data ◽  
The Individual ◽  
The City

<p><strong>Abstract.</strong> In this paper, we use Floating Car Data from the city of Shanghai and Fuzzy Inference model to detect congestion indexes throughout the city. We aim to investigate to which extent traffic congestion is severe during afternoon rush hour. Additionally, we compare our results to the ones obtained by calculating congestion indexes on conventional way. Although we do not argue that our model is the best measure of congestion, it does allow the mechanism to combine different measures and to incorporate the uncertainty in the individual measures so that the compound picture of congestion can be reproduced.</p>

scholarly journals A Policy-Sensitive Model of Parking Choice for Commercial Vehicles in Urban Areas

2020 ◽  
Vol 54 (3) ◽  
pp. 606-630 ◽  
Author(s):  
Giacomo Dalla Chiara ◽  
Lynette Cheah ◽  
Carlos Lima Azevedo ◽  
Moshe E. Ben-Akiva
Keyword(s):  
Traffic Congestion ◽  
Urban Areas ◽  
Commercial Vehicle ◽  
Choice Model ◽  
Discrete Event ◽  
Real Data ◽  
Behavior Modeling ◽  
Model Parameters ◽  
Commercial Vehicles ◽  
Real World Data

Understanding factors that drive the parking choice of commercial vehicles at delivery stops in cities can enhance logistics operations and the management of freight parking infrastructure, mitigate illegal parking, and ultimately reduce traffic congestion. In this paper, we focus on this decision-making process at large urban freight traffic generators, such as retail malls and transit terminals, that attract a large share of urban commercial vehicle traffic. Existing literature on parking behavior modeling has focused on passenger vehicles. This paper presents a discrete choice model for commercial vehicle parking choice in urban areas. The model parameters were estimated by using detailed, real-world data on commercial vehicle parking choices collected in two commercial urban areas in Singapore. The model analyzes the effect of several variables on the parking behavior of commercial vehicle drivers, including the presence of congestion and queueing, attitudes toward illegal parking, and pricing (parking fees). The model was validated against real data and applied within a discrete-event simulation to test the economic and environmental impacts of several parking measures, including pricing strategies and parking enforcement.

scholarly journals Improving traffic and emergency vehicle clearence at congested intersections using fuzzy inference engine

2021 ◽  
Vol 11 (4) ◽  
pp. 3176
Author(s):  
Aditi Agrawal ◽  
Rajeev Paulus
Keyword(s):  
Control System ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Fuzzy Inference ◽  
Heavy Traffic ◽  
Green Light ◽  
Emergency Vehicle ◽  
Traffic Light ◽  
Traffic Condition ◽  
Traffic Lights

Traffic signals play an important role in controlling and coordinating the traffic movement in cities especially in urban areas. As the traffic is exponentially increasing in cities and the pre-timed traffic light control is insufficient in effective timing of the traffic lights, it leads to poor traffic clearance and ultimately to heavy traffic congestion at intersections. Even the Emergency vehicles like Ambulance and Fire brigade are struck at such intersections and experience a prolonged waiting time. An adaptive and intelligent approach in design of traffic light signals is desirable and this paper contributes in applying fuzzy logic to control traffic signal of single four-way intersection giving priority to the Emergency vehicle clearance. The proposed control system is composed of two parallel controllers to select the appropriate lane for green signal and also to decide the appropriate green light time as per the real time traffic condition. Performance of the proposed system is evaluated by using simulations and comparing with pre-timed control system in changing traffic flow condition. Simulation results show significant improvement over the pre-timed control in terms of traffic clearance and lowering of Emergency vehicle wait time at the intersection especially when traffic intensity is high.

Travel Costs and Intermodal Distribution in Urban Transportation

1998 ◽  
Vol 1649 (1) ◽  
pp. 105-112 ◽  
Author(s):  
Vukan R. Vuchic ◽  
Young-Jae Lee ◽  
Yong Eun Shin
Keyword(s):  
Low Income ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Indirect Costs ◽  
Modal Shift ◽  
Travel Costs ◽  
Gasoline Taxes ◽  
The Individual ◽  
Automobile Use ◽  
Automobile Travel

The free selection of travel between automobile and transit modes results in the individual equilibrium condition, which is not socially optimal. It is shown that shifting travel from cars to transit under most conditions results in travel improvements for both modes. To implement this winwin change, however, it is necessary to decrease the disutility (cost) of travel by transit and increase the disutility of automobile travel. A comparative analysis of travel costs by different modes shows that automobile users pay extremely low out-of-pocket costs, particularly when parking is subsidized (free). Indirect costs and impacts of automobile travel in urban areas are very high, but users do not pay them. This condition of underpriced automobile use results in excessive driving, which causes traffic congestion and has many negative impacts on cities. In many cities, transit improvements or incentives are paralleled by automobile incentives; this represents subsidization of competing services and thus fails to induce modal shift. A shift of travel from cars to transit (and other modes) can best be achieved by car disincentives complemented by transit improvements, so that travelers can change modes rather than reduce essential trips. The mobility of the low-income population can be enhanced when revenue from automobile disincentives is applied to improvements of alternative modes. Measures that reduce subsidies to automobile use and convert them into direct user costs, such as a significant increase in gasoline taxes and a reduction of tax exemptions for many car trips, are both effective and equitable.

Fuzzy peak hour for urban road traffic network

2015 ◽  
Vol 29 (15) ◽  
pp. 1550074 ◽  
Author(s):  
Zhao Tian ◽  
Li-Min Jia ◽  
Hong-Hui Dong ◽  
Zun-Dong Zhang ◽  
Yang-Dong Ye
Keyword(s):  
Traffic Congestion ◽  
Urban Areas ◽  
Road Traffic ◽  
Flow Speed ◽  
Urban Traffic ◽  
Traffic Network ◽  
Traffic Condition ◽  
Time Pattern ◽  
Urban Road ◽  
Optimal Separation

Traffic congestion is now nearly ubiquitous in many urban areas and frequently occurs during rush hour periods. Rush hour avoidance is an effective way to ease traffic congestion. It is significant to calculate the rush hour for alleviating traffic congestion. This paper provides a method to calculate the fuzzy peak hour of the urban traffic network considering the flow, speed and occupancy. The process of calculation is based on betweenness centrality of network theory, optimal separation method, time period weighting, probability–possibility transformations and trapezoidal approximations of fuzzy numbers. The fuzzy peak hour of the urban road traffic network (URTN) is a trapezoidal fuzzy number [m1, m2, m3, m4]. It helps us (i) to confirm a more detailed traffic condition at each moment, (ii) to distinguish the five traffic states of the traffic network in one day, (iii) to analyze the characteristic of appearance and disappearance processes of the each traffic state and (iv) to find out the time pattern of residents travel in one city.

GIS-Based Urban Land Use and Traffic Coordination Evaluation System

2015 ◽  
Vol 738-739 ◽  
pp. 204-208 ◽  
Author(s):  
Yu Jie Sun ◽  
Wen Xin Qiao ◽  
Yong Sheng Zhang ◽  
Yang Yang
Keyword(s):  
Land Use ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Evaluation System ◽  
Supply And Demand ◽  
Network Capacity ◽  
Area Ratio ◽  
Urban Land Use ◽  
Urban Traffic ◽  
Planning Scheme

To relieve the traffic congestion in urban areas, one approach is to modify the current planning scheme based on the balance between traffic supply and demand. Based on the proposed GIS platform, this paper analyzes the interactive relationship between land use and traffic system. Feasibility judgment of planning scheme is implemented based on land attribute, represented by urban floor area ratio and road area ratio. TransCAD based simulation is used to evaluate the planning scheme by traffic flow and the saturation of each link. Comparisons between traffic generation and traffic network capacity, based on a two-dimensional model of Space and Time Consuming Method, is presented to verify the coordination between urban traffic and land use. A real-world case study is implemented to test the efficiency and applicability of the proposed model and computing methods.

Smart Driving System for Improving Traffic Flow

2017 ◽  
Vol 7 (7) ◽  
pp. 236
Author(s):  
Rajesh Kumar Gupta ◽  
L. N. Padhy ◽  
Sanjay Kumar Padhi
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Adaptive Cruise Control ◽  
Human Perception ◽  
Cruise Control ◽  
Driving System ◽  
V2v Communication ◽  
Traffic Conditions ◽  
Cooperative Adaptive Cruise Control

Traffic congestion on road networks is one of the most significant problems that is faced in almost all urban areas. Driving under traffic congestion compels frequent idling, acceleration, and braking, which increase energy consumption and wear and tear on vehicles. By efficiently maneuvering vehicles, traffic flow can be improved. An Adaptive Cruise Control (ACC) system in a car automatically detects its leading vehicle and adjusts the headway by using both the throttle and the brake. Conventional ACC systems are not suitable in congested traffic conditions due to their response delay.  For this purpose, development of smart technologies that contribute to improved traffic flow, throughput and safety is needed. In today’s traffic, to achieve the safe inter-vehicle distance, improve safety, avoid congestion and the limited human perception of traffic conditions and human reaction characteristics constrains should be analyzed. In addition, erroneous human driving conditions may generate shockwaves in addition which causes traffic flow instabilities. In this paper to achieve inter-vehicle distance and improved throughput, we consider Cooperative Adaptive Cruise Control (CACC) system. CACC is then implemented in Smart Driving System. For better Performance, wireless communication is used to exchange Information of individual vehicle. By introducing vehicle to vehicle (V2V) communication and vehicle to roadside infrastructure (V2R) communications, the vehicle gets information not only from its previous and following vehicle but also from the vehicles in front of the previous Vehicle and following vehicle. This enables a vehicle to follow its predecessor at a closer distance under tighter control.

Empirical Assessment and Modeling of Traffic-induced Air Pollution

2021 ◽  
pp. 036119812110237
Author(s):  
Zhongyang Lu ◽  
Andy H. F. Chow ◽  
Jacky Leung ◽  
Haydn Kwok ◽  
Sammy Cheung
Keyword(s):  
Air Pollution ◽  
Hong Kong ◽  
Road Network ◽  
Historical Data ◽  
Flow Speed ◽  
Traffic Flows ◽  
Real World Data ◽  
Empirical Assessment ◽  
Air Quality Monitoring Stations ◽  
Transport Management

Congestion and traffic-induced air pollution are associated with population growth and economic development. Compared with congestion, there are relatively few studies on modeling and assessment of traffic-induced pollution. This paper presents an empirical assessment and analysis of traffic-induced air pollution with real-world data collected from the Hong Kong Strategic Road Network. The study employed historical data of traffic flows, speeds, and emission of air pollutants collated by the Hong Kong Transport Department and Environmental Protection Department. This paper first reveals the correlation between traffic flows, speeds, and corresponding induced pollutants including nitrogen oxides (NO2, NOX) and carbon monoxide (CO). To gain further statistical insight, a regression analysis was conducted on the flow–speed–emission relationship at three air quality monitoring stations, which revealed the significance of various factors on this relationship. This study contributes to green transport management and urban sustainability.

scholarly journals Constrained Urban Airspace Design for Large-Scale Drone-Based Delivery Traffic

Aerospace ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. 38
Author(s):  
Malik Doole ◽  
Joost Ellerbroek ◽  
Victor L. Knoop ◽  
Jacco M. Hoekstra
Keyword(s):  
Traffic Congestion ◽  
Urban Areas ◽  
Large Scale ◽  
Cost Savings ◽  
Future Research ◽  
Fast Time ◽  
Potential Cost ◽  
Horizontal Structure ◽  
Traffic Demand ◽  
Demand Patterns

Large-scale adoption of drone-based delivery in urban areas promise societal benefits with respect to emissions and on-ground traffic congestion, as well as potential cost savings for drone-based logistic companies. However, for this to materialise, the ability of accommodating high volumes of drone traffic in an urban airspace is one of the biggest challenges. For unconstrained airspace, it has been shown that traffic alignment and segmentation can be used to mitigate conflict probability. The current study investigates the application of these principles to a highly constrained airspace. We propose two urban airspace concepts, applying road-based analogies of two-way and one-way streets by imposing horizontal structure. Both of the airspace concepts employ heading-altitude rules to vertically segment cruising traffic according to their travel direction. These airspace configurations also feature transition altitudes to accommodate turning flights that need to decrease the flight speed in order to make safe turns at intersections. While using fast-time simulation experiments, the performance of these airspace concepts is compared and evaluated for multiple traffic demand densities in terms of safety, stability, and efficiency. The results reveal that an effective way to structure drone traffic in a constrained urban area is to have vertically segmented altitude layers with respect to travel direction as well as horizontal constraints imposed to the flow of traffic. The study also makes recommendations for areas of future research, which are aimed at supporting dynamic traffic demand patterns.

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