A Road Network Design Model for Large-Scale Urban Network

2014 ◽  
pp. 139-149
Author(s):  
Ernesto Cipriani ◽  
Andrea Gemma ◽  
Marialisa Nigro
Keyword(s):  
Network Design ◽  
Road Network ◽  
Large Scale ◽  
Design Model ◽  
Urban Network

Demonstrative Case of a Pedestrian Network Design Model Considering Effects on Motorized Traffic

2020 ◽  
Vol 2674 (7) ◽  
pp. 373-384
Author(s):  
Christina Iliopoulou ◽  
Maria Tseliou ◽  
Konstantinos Kepaptsoglou ◽  
Stratos Papadimitriou
Keyword(s):  
Network Design ◽  
Road Network ◽  
Ad Hoc ◽  
Decisive Role ◽  
Design Guidelines ◽  
Design Model ◽  
Urban Network ◽  
Urban Networks ◽  
The Road ◽  
Pedestrian Network

The transformation of urban roadways into pedestrian streets is a popular measure for reshaping city parts and enhancing their livability. Nevertheless, pedestrianization schemes are expected to have some impact on the performance of the neighboring road network, especially if these are established ad-hoc or solely based on non-transport criteria. This study introduces a methodological tool for supporting decisions on implementing pedestrianization schemes in urban networks. A bi-level network design model variant is developed for that purpose, whose design objective is to maximize the extent of pedestrian streets in an urban network, while maintaining acceptable impacts to the performance of the road network. Alternative decisions on pedestrianization are considered for each network segment; these include partial (one-directional) or complete (bi-directional) pedestrianization under physical and operational criteria and constraints. The model is applied for a mid-sized urban network in Greece and solved using a genetic algorithm. Results show that the pedestrianization of almost 7% of the road network in relation to length leads to a 40% increase in total network travel time, while a higher ratio of complete versus partial pedestrianization is more advantageous. Outcomes also reveal that that rigid design guidelines should be examined in a case-by-case approach, as superior results may be attained if some constraints, such as those related to the overall street width, are relaxed. Reasonably, policy priorities significantly impact generated solutions and are expected to play a decisive role in the design of pedestrianization schemes.

A road network design model considering horizontal and vertical equity: Evidences from an empirical study

2017 ◽  
Vol 5 (2) ◽  
pp. 392-399 ◽  
Author(s):  
Leonardo Caggiani ◽  
Rosalia Camporeale ◽  
Mario Binetti ◽  
Michele Ottomanelli
Keyword(s):  
Empirical Study ◽  
Network Design ◽  
Road Network ◽  
Design Model ◽  
Vertical Equity

scholarly journals Integrating Equity Objectives in a Road Network Design Model

2008 ◽  
Vol 2089 (1) ◽  
pp. 35-42 ◽  
Author(s):  
Bruno Santos ◽  
António Antunes ◽  
Eric J. Miller
Keyword(s):  
Network Design ◽  
Road Network ◽  
Design Model

scholarly journals Improving the Performance of the Bilevel Solution for the Continuous Network Design Problem

2018 ◽  
Vol 30 (6) ◽  
pp. 709-720
Author(s):  
Ozgur Baskan ◽  
Cenk Ozan ◽  
Mauro Dell’Orco ◽  
Mario Marinelli
Keyword(s):  
Network Design ◽  
Traffic Congestion ◽  
Design Problem ◽  
Road Network ◽  
Large Scale ◽  
Critical Role ◽  
Differential Evolution Algorithm ◽  
Global Optimum ◽  
Network Design Problem ◽  
Local Authorities

For a long time, many researchers have investigated the continuous network design problem (CNDP) to distribute equitably additional capacity between selected links in a road network, to overcome traffic congestion in urban roads. In addition, CNDP plays a critical role for local authorities in tackling traffic congestion with a limited budget. Due to the mutual interaction between road users and local authorities, CNDP is usually solved using the bilevel modeling technique. The upper level seeks to find the optimal capacity enhancements of selected links, while the lower level is used to solve the traffic assignment problem. In this study, we introduced the enhanced differential evolution algorithm based on multiple improvement strategies (EDEMIS) for solving CNDP. We applied EDEMIS first to a hypothetical network to show its ability in finding the global optimum solution, at least in a small network. Then, we used a 16-link network to reveal the capability of EDEMIS especially in the case of high demand. Finally, we used the Sioux Falls city network to evaluate the performance of EDEMIS according to other solution methods on a medium-sized road network. The results showed that EDEMIS produces better solutions than other considered algorithms, encouraging transportation planners to use it in large-scale road networks.

scholarly journals Large-Scale Road Network Congestion Pattern Analysis and Prediction Using Deep Convolutional Autoencoder

2021 ◽  
Vol 13 (9) ◽  
pp. 5108
Author(s):  
Navin Ranjan ◽  
Sovit Bhandari ◽  
Pervez Khan ◽  
Youn-Sik Hong ◽  
Hoon Kim
Keyword(s):  
Traffic Congestion ◽  
Network Architecture ◽  
Road Network ◽  
Large Scale ◽  
Pattern Analysis ◽  
Image Data ◽  
Urban Life ◽  
Prediction Algorithm ◽  
Rapid Urbanization ◽  
Convolutional Autoencoder

The transportation system, especially the road network, is the backbone of any modern economy. However, with rapid urbanization, the congestion level has surged drastically, causing a direct effect on the quality of urban life, the environment, and the economy. In this paper, we propose (i) an inexpensive and efficient Traffic Congestion Pattern Analysis algorithm based on Image Processing, which identifies the group of roads in a network that suffers from reoccurring congestion; (ii) deep neural network architecture, formed from Convolutional Autoencoder, which learns both spatial and temporal relationships from the sequence of image data to predict the city-wide grid congestion index. Our experiment shows that both algorithms are efficient because the pattern analysis is based on the basic operations of arithmetic, whereas the prediction algorithm outperforms two other deep neural networks (Convolutional Recurrent Autoencoder and ConvLSTM) in terms of large-scale traffic network prediction performance. A case study was conducted on the dataset from Seoul city.

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