scholarly journals Sustainable Traffic Management in an Urban Area: An Integrated Framework for Real-Time Traffic Control and Route Guidance Design

2020 ◽  
Vol 12 (2) ◽  
pp. 726 ◽  
Author(s):  
Stefano de Luca ◽  
Roberta Di Pace ◽  
Silvio Memoli ◽  
Luigi Pariota
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Network Performance ◽  
Impact Analysis ◽  
Control Method ◽  
Choice Model ◽  
Route Guidance ◽  
Modeling Framework ◽  
Integrated Framework

This paper focuses on the presentation of an integrated framework based on two advanced strategies, aimed at mitigating the effect of traffic congestion in terms of performance and environmental impact. In particular, the paper investigates the “operational benefits” that can be derived from the combination of traffic control (TC) and route guidance (RG) strategies. The framework is based on two modules and integrates a within-day traffic control method and a day-to-day behavioral route choice model. The former module consists of an enhanced traffic control model that can be applied to design traffic signal decision variables, suitable for real-time optimization. The latter designs the information consistently with predictive user reactions to the information itself. The proposed framework is implemented to a highly congested sub-network in the city center of Naples (Italy) and different scenarios are tested and compared. The “do nothing” scenario (current; DN) and the “modeled compliance” (MC) scenario, in which travelers’ reaction to the information (i.e., compliance) is explicitly represented. In order to evaluate the effectiveness of the proposed strategy and the modeling framework, the following analyses are carried out: (i) Network performance analysis; (ii) system convergence and stability analysis, as well as the compliance evolution over time; (iii) and emissions and fuel consumption impact analysis.

scholarly journals Self-Regulating Demand and Supply Equilibrium in Joint Simulation of Travel Demand and a Ride-Pooling Service

2021 ◽  
pp. 036119812199714
Author(s):  
Gabriel Wilkes ◽  
Roman Engelhardt ◽  
Lars Briem ◽  
Florian Dandl ◽  
Peter Vortisch ◽  
...  
Keyword(s):  
Real Time ◽  
Travel Demand ◽  
State Of The Art ◽  
Choice Model ◽  
Mode Choice ◽  
Attractive Alternative ◽  
Full Potential ◽  
Demand Model ◽  
Modeling Framework ◽  
Demand And Supply

This paper presents the coupling of a state-of-the-art ride-pooling fleet simulation package with the mobiTopp travel demand modeling framework. The coupling of both models enables a detailed agent- and activity-based demand model, in which travelers have the option to use ride-pooling based on real-time offers of an optimized ride-pooling operation. On the one hand, this approach allows the application of detailed mode-choice models based on agent-level attributes coming from mobiTopp functionalities. On the other hand, existing state-of-the-art ride-pooling optimization can be applied to utilize the full potential of ride-pooling. The introduced interface allows mode choice based on real-time fleet information and thereby does not require multiple iterations per simulated day to achieve a balance of ride-pooling demand and supply. The introduced methodology is applied to a case study of an example model where in total approximately 70,000 trips are performed. Simulations with a simplified mode-choice model with varying fleet size (0–150 vehicles), fares, and further fleet operators’ settings show that (i) ride-pooling can be a very attractive alternative to existing modes and (ii) the fare model can affect the mode shifts to ride-pooling. Depending on the scenario, the mode share of ride-pooling is between 7.6% and 16.8% and the average distance-weighed occupancy of the ride-pooling fleet varies between 0.75 and 1.17.

Analytical Modeling of Driver-Guidance Schemes with Flow Variability Considerations

2000 ◽  
Vol 1717 (1) ◽  
pp. 55-65 ◽  
Author(s):  
Isam Kaysi ◽  
Nadine Hage Ali
Keyword(s):  
Traffic Control ◽  
Traffic Congestion ◽  
User Equilibrium ◽  
Open Loop ◽  
Route Guidance ◽  
Traveler Information ◽  
Predictive Information ◽  
Flow Variability ◽  
Traveler Information Systems ◽  
Alternate Routes

The role that advanced traveler information systems (ATISs) are likely to play in alleviating traffic congestion is explored. The impacts of and benefits from traveler guidance systems that are based on instantaneous as well as predictive information are assessed by developing an analytical formulation for a simple prototypical network. Previous research introducing day-to-day flow variability with both compliance and market-penetration considerations is reviewed, and the case in which traffic flow varies within the day is developed. Two strategies for determining route-guidance directives are considered in the case of predictive information. Also, the integration of ATIS and traffic control through open-loop coordination measures is introduced, with the objective of finding the optimal signal control to maintain user equilibrium on alternate routes. The superiority of predictive information in maintaining guidance validity and mitigating the potential adverse impacts of information is demonstrated.

scholarly journals Impact Analysis of Land Use on Traffic Congestion Using Real-Time Traffic and POI

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Tianqi Zhang ◽  
Lishan Sun ◽  
Liya Yao ◽  
Jian Rong
Keyword(s):  
Land Use ◽  
Regression Analysis ◽  
Linear Regression ◽  
Real Time ◽  
Traffic Congestion ◽  
Impact Analysis ◽  
Linear Regression Analysis ◽  
Urban Land Use ◽  
Real Time Traffic ◽  
Map Data

This paper proposed a new method to describe, compare, and classify the traffic congestion points in Beijing, China, by using the online map data and further revealed the relationship between traffic congestion and land use. The data of the point of interest (POI) and the real-time traffic was extracted from an electronic map of the area in the fourth ring road of Beijing. The POIs were quantified based on the architectural area of the land use; the congestion points were identified based on real-time traffic. Then, the cluster analysis using the attributes of congestion time was conducted to identify the main traffic congestion areas. The result of a linear regression analysis between the congestion time and the land use showed that the influence of the high proportion of commercial land use on the traffic congestion was significant. Also, we considered five types of land use through performing a linear regression analysis between the congestion time and the ratio of four types of land use. The results showed that the reasonable ratio of land use types could efficiently reduce congestion time. This study makes contributions to the policy-making of urban land use.

scholarly journals Adaptive Traffic Management System using CNN (YOLO)

2021 ◽  
Vol 9 (VI) ◽  
pp. 3726-3732
Author(s):  
B. Sowmya
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Object Identification ◽  
Traffic Signal ◽  
Traffic Density ◽  
Loop Control ◽  
Control Framework

The huge number of vehicles on the roadways is making congestion a significant problem. The line longitudinal vehicle waiting to be processed at the crossroads increases quickly, and the traditionally used traffic signals are not able to program it properly. Manual traffic monitoring may be an onerous job since a number of cameras are deployed over the network in traffic management centers. The proactive decision-making of human operators, which would decrease the effect of events and recurring road congestion, might contribute to the easing of the strain of automation.The traffic control frameworks in India are now needed as it is an open-loop control framework, without any input or detection mechanism. Inductive loops and sensors employed in existing technology used to detect the number of passing vehicles. The way traffic lights are adapted is highly inefficient and costly in this existing technology. The aim was to build a traffic control framework by introducing a system for detection ,which gives an input to the existing system (closed loop control system) in order to adapt to the changing traffic density patterns and to provide the controller with a crucial indication for ongoing activities. By this technique, the improvement of the signals on street is extended and thus saves time by preventing traffic congestion. This study proposes an algorithm for real-time traffic signal control, depending on the traffic flow. In reality, the features of competitive traffic flow at the signposted road crossing are used by computer vision and by machine learning. This is done by the latest, real-time object identification, based on convolutional Neural Networks network called You Look Once (YOLO). Traffic signal phases are then improved by data acquired in order to allow more vehicles to pass safely over minimal wait times, particularly the line long and the time of waiting per vehicle.This adjustable traffic signal timer is used to calculate traffic density utilizing YOLO object identification using live pictures of cameras in intervals and adjusts the signal timers appropriately, therefore decreasing the road traffic congestion, ensuring speedier transit for persons, and reducing fuel consumption. The traffic conditions will improve enormously at a relatively modest cost. Inductive loops are a viable but costly approach. This method thereby cuts expenses and outcomes quickly.

scholarly journals Design and Development of Traffic Control System using Image Processing

2021 ◽  
Vol 9 (VI) ◽  
pp. 3247-3252
Author(s):  
G. Kalyan
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Red Light ◽  
Traffic Density ◽  
The Road ◽  
Real Time Traffic ◽  
On The Road

Traffic congestion is now a big issue. Although it seems to penetrate throughout the world, urban towns are the ones which are most effected. And it is expanding in nature that it is necessary to understand the density of roads in real time to better regulate signals and efficient management of transport. Various traffic congestions, such as limited capacity, unrestricted demand, huge Red Light waits might occur. While insufficient capacity and unlimited demand are somehow interconnected, their delay in lighting is difficult to encode and not traffic dependant. The necessity to simulate and optimise traffic controls therefore arises in order to better meet this growing demand. The traffic management of information, ramp metering, and updates in real-time has been frequently used in recent years for image processing and monitoring systems. An image processing can also be used for the traffic density estimation. This research describes the approach for the computation of real-time traffic density by image processing for using live picture feed from cameras. It focuses also on the algorithm for the transmission of traffic signals on the road according to the density of vehicles and therefore aims to reduce road congestion, which reduces the number of accidents.

scholarly journals Expressive Real-Time Intersection Scheduling

2019 ◽  
Vol 33 ◽  
pp. 9882-9883
Author(s):  
Rick Goldstein
Keyword(s):  
Real Time ◽  
Traffic Congestion ◽  
Metropolitan Areas ◽  
Control Method ◽  
Traffic Data ◽  
Traffic Lights ◽  
Traffic Conditions ◽  
Real Time Traffic ◽  
Scheduling Strategies ◽  
Negative Impacts

Traffic congestion is a widespread annoyance throughout global metropolitan areas. It causes increases in travel time, increases in emissions, inefficient usage of gasoline, and driver frustration. Inefficient signal patterns at traffic lights are one major cause of such congestion. Intersection scheduling strategies that make real-time decisions to extend or end a green signal based on real-time traffic data offer one opportunity reduce congestion and its negative impacts. My research proposes Expressive Real-time Intersection Scheduling (ERIS). ERIS is a decentralized, schedule-driven control method which makes a decision every second based on current traffic conditions to reduce congestion.

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