scholarly journals Calibration of MATSim in the context of natural hazards in Belgium

2016 ◽  
Author(s):  
Mario Cools ◽  
Ismaïl Saadi ◽  
Ahmed Mustafa ◽  
Jacques Teller
Keyword(s):  
Travel Behavior ◽  
Large Scale ◽  
Travel Demand ◽  
Scale Effects ◽  
Transportation Network ◽  
Transportation Systems ◽  
Generation Process ◽  
Traffic Pattern ◽  
Synthetic Population ◽  
River Floods

In Belgium, river floods are among the most frequent natural disasters and they may cause important changes on travel demand. In this regard, we propose to set up a large scale scenario using MATSim for guarantying an accurate assessment of the river floods impact on the transportation systems. In terms of inputs, agent-based models require a base year population. In this context, a synthetic population with a respective set of attributes is generated as a key input. Afterwards, agents are assigned activity chains through an activity-based generation process. Finally, the synthetic population and the transportation network are integrated into the dynamic traffic assignment simulator, i.e. MATSim. With respect to data, households travel surveys are the main inputs for synthesizing the populations. Besides, a steady-state inundation map is integrated within MATSim for simulating river floods. To our knowledge, very few studies have focused on how river floods affect transportation systems. In this regard, this research will undoubtedly provide new insights in term of methodology and traffic pattern analysis under disruptions, especially with regard to spatial scale effects. The results indicate that at the municipality level, it is possible to capture the effects of disruptions on travel behavior. In this context, further disaggregation is needed in future studies for identifying to what extent results are sensitive to disaggregation. In addition, results also suggest that the target sub-population exposed to flood risk should be isolated from the rest of the travel demand to reach have more sensitive effects.DOI: http://dx.doi.org/10.4995/CIT2016.2016.4098

scholarly journals An Open GMNS Dataset of a Dynamic Multi-Modal Transportation Network Model of Melbourne, Australia

Data ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 21
Author(s):  
Fatemeh Nourmohammadi ◽  
Mohammadhadi Mansourianfar ◽  
Sajjad Shafiei ◽  
Ziyuan Gu ◽  
Meead Saberi
Keyword(s):  
Network Model ◽  
Large Scale ◽  
Travel Demand ◽  
Dynamic Traffic Assignment ◽  
Transportation Network ◽  
Transportation Systems ◽  
Data Intensive ◽  
Complex Interactions ◽  
Planning Models ◽  
Large Scale Networks

Simulation-based dynamic traffic assignment models are increasingly used in urban transportation systems analysis and planning. They replicate traffic dynamics across transportation networks by capturing the complex interactions between travel demand and supply. However, their applications particularly for large-scale networks have been hindered by the challenges associated with the collection, parsing, development, and sharing of data-intensive inputs. In this paper, we develop and share an open dataset for reproduction of a dynamic multi-modal transportation network model of Melbourne, Australia. The dataset is developed consistently with the General Modeling Network Specification (GMNS), enabling software-agnostic human and machine readability. GMNS is a standard readable format for sharing routable transportation network data that is designed to be used in multimodal static and dynamic transportation operations and planning models.

scholarly journals Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2229 ◽  
Author(s):  
Sen Zhang ◽  
Yong Yao ◽  
Jie Hu ◽  
Yong Zhao ◽  
Shaobo Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Large Scale ◽  
Transportation Networks ◽  
Transportation Network ◽  
Transportation Systems ◽  
Neural Network Models ◽  
Computation Efficiency ◽  
Congestion Prediction

Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available online traffic service provider Washington State Department of Transportation. We then propose a deep autoencoder-based neural network model with symmetrical layers for the encoder and the decoder to learn temporal correlations of a transportation network and predicting traffic congestion. Our experimental results on the SATCS dataset show that the proposed DCPN model can efficiently and effectively learn temporal relationships of congestion levels of the transportation network for traffic congestion forecasting. Our method outperforms two other state-of-the-art neural network models in prediction performance, generalization capability, and computation efficiency.

The Characteristics of Resident Travel in Beijing Xizhimen Transport Terminal

2011 ◽  
Vol 225-226 ◽  
pp. 1212-1217
Author(s):  
Xue Mei Li ◽  
Jing Yin ◽  
Qian Che
Keyword(s):  
Travel Behavior ◽  
Transportation Planning ◽  
Large Scale ◽  
Transportation Network ◽  
Urban Transportation ◽  
The Other ◽  
Theoretical Support ◽  
Controllable Factors ◽  
Disaggregate Model ◽  
Core Facilities

Transport Terminals are core facilities of urban transportation system, and the joint of different transportation in urban transportation network. Because of their functions and place, they are faced with huge traffic pressure. So the research about the characteristics of resident travel in transport terminals could provide theoretical support for urban transportation planning, organizing and managing, finally improve urban transportation satisfaction among residents. Against this background, Xizhimen as the research object is a representative transport terminal in Beijing. Based on large-scale investigation, on one hand, the characteristics of residents travel behavior are analyzed qualitatively; on the other hand, by building the Disaggregate Model, analyze the utility functions of different travel modes quantitatively, to find some controllable factors to optimize transport terminal and improve their satisfaction.

Transit Travel Demand Analysis Method Based on APTS Multi-Source Data

2013 ◽  
Vol 361-363 ◽  
pp. 2122-2126
Author(s):  
Jun Chen ◽  
Xiao Hua Li ◽  
Lan Ma
Keyword(s):  
Public Transportation ◽  
Large Scale ◽  
Travel Demand ◽  
Low Cost ◽  
Transportation Systems ◽  
Demand Analysis ◽  
Sample Survey ◽  
Analysis Method ◽  
Source Data ◽  
Bus Stops

Traditional transit travel information is acquired by Trip Sample Survey which has some disadvantages including high cost and short data lifecycle. This paper researched transit travel demand analysis method using Advanced Public Transportation Systems (APTS) data. The study collected APTS data of Nanning City in China and established APTS multi-source data analysis platform applying data warehouse technology. Based on key problems research, the paper presented the analysis procedure and content. Then, this study proposed the core algorithms of the method which are determinations of boarding bus stops, alighting bus stops and transfer bus stops of smart card passengers. Finally, these algorithms programs are experimented using large scale practical APTS data. The results show that this analysis method is low cost, operability and high accuracy.

Integrating Supply and Demand Perspectives for a Large-Scale Simulation of Shared Autonomous Vehicles

2020 ◽  
Vol 2674 (7) ◽  
pp. 181-192 ◽  
Author(s):  
Krishna Murthy Gurumurthy ◽  
Felipe de Souza ◽  
Annesha Enam ◽  
Joshua Auld
Keyword(s):  
Autonomous Vehicles ◽  
High Performance ◽  
Large Scale ◽  
Choice Model ◽  
Transportation Network ◽  
Supply And Demand ◽  
Transportation Systems ◽  
Point Of View ◽  
Computational Time ◽  
Vehicle Assignment

Transportation Network Companies (TNCs) have been steadily increasing the share of total trips in metropolitan areas across the world. Micro-modeling TNC operation is essential for large-scale transportation systems simulation. In this study, an agent-based approach for analyzing supply and demand aspects of ride-sourcing operation is done using POLARIS, a high-performance simulation tool. On the demand side, a mode-choice model for the agent and a vehicle-ownership model that informs this choice are developed. On the supply side, TNC vehicle-assignment strategies, pick-up and drop-off operations, and vehicle repositioning are modeled with congestion feedback, an outcome of the mesoscopic traffic simulation. Two case studies of Bloomington and Chicago in Illinois are used to study the framework’s computational speed for large-scale operations and the effect of TNC fleets on a region’s congestion patterns. Simulation results show that a zone-based vehicle-assignment strategy scales better than relying on matching closest vehicles to requests. For large regions like Chicago, large fleets are seen to be detrimental to congestion, especially in a future in which more travelers will use TNCs. From an operational point of view, an efficient relocation strategy is critical for large regions with concentrated demand, but not regulating repositioning can worsen empty travel and, consequently, congestion. The TNC simulation framework developed in this study is of special interest to cities and regions, since it can be used to model both demand and supply aspects for large regions at scale, and in reasonably low computational time.

scholarly journals Electromagnetic Energy Harvesting Technology: Key to Sustainability in Transportation Systems

2019 ◽  
Vol 11 (18) ◽  
pp. 4906 ◽  
Author(s):  
Mohammadreza Gholikhani ◽  
Seyed Amid Tahami ◽  
Mohammadreza Khalili ◽  
Samer Dessouky
Keyword(s):  
Energy Harvesting ◽  
Large Scale ◽  
Scale Effects ◽  
Electrical Power ◽  
Experimental Tests ◽  
Transportation Systems ◽  
Electromagnetic Energy ◽  
Sustainable Transportation ◽  
Traffic Conditions ◽  
Electromagnetic Energy Harvesting

The convergence of concerns about environmental quality, economic vitality, social equity, and climate change have led to vast interest in the concept of sustainability. Energy harvesting from roadways is an innovative way to provide green and renewable energy for sustainable transportation. However, energy harvesting technologies are in their infancy, so limited studies were conducted to evaluate their performance. This article introduces innovative electromagnetic energy harvesting technology that includes two different mechanisms to generate electrical power: a cantilever generator mechanism and a rotational mechanism. Laboratory experimental tests were conducted to examine the performance of the two mechanisms in generating power under different simulated traffic conditions. The experimental results had approximately root mean square power 0.43 W and 0.04 W and maximum power of 2.8 W and 0.25 W for cantilever and rotational, respectively. These results showed promising capability for both mechanisms in generating power under real traffic conditions. In addition, the study revealed the potential benefits of energy harvesting from roadways to support sustainability in transportation systems. Overall, the findings show that energy harvesting can impact sustainable transportation systems significantly. However, further examination of the large-scale effects of energy harvesting from roadways on sustainability is needed.

scholarly journals Retrofitting Transportation Network Using a Fuzzy Random Multiobjective Bilevel Model to Hedge against Seismic Risk

2014 ◽  
Vol 2014 ◽  
pp. 1-24 ◽  
Author(s):  
Lu Gan ◽  
Jiuping Xu
Keyword(s):  
Seismic Risk ◽  
Construction Projects ◽  
Large Scale ◽  
Programming Model ◽  
Transportation Network ◽  
Fuzzy Variable ◽  
Fuzzy Random Variable ◽  
Random Variable ◽  
Transportation Systems ◽  
Fuzzy Random

This paper focuses on the problem of hedging against seismic risk through the retrofit of transportation systems in large-scale construction projects (LSCP). A fuzzy random multiobjective bilevel programming model is formulated with the objectives of the retrofit costs and the benefits on two separate levels. After establishing the model, a fuzzy random variable transformation approach and fuzzy variable approximation decomposition are used to deal with the uncertainty. An approximation decomposition-based multi-objective AGLNPSO is developed to solve the model. The results of a case study validate the efficiency of the proposed approach.

Incorporating Land Use into Methods of Synthetic Population Generation and of Transfer of Behavioral Data

2017 ◽  
Vol 2668 (1) ◽  
pp. 11-20 ◽  
Author(s):  
Elizabeth C. McBride ◽  
Adam W. Davis ◽  
Jae Hyun Lee ◽  
Konstadinos G. Goulias
Keyword(s):  
Land Use ◽  
Travel Behavior ◽  
Travel Demand ◽  
Behavioral Data ◽  
Synthesis Methods ◽  
Population Synthesis ◽  
Household Travel Survey ◽  
Synthetic Populations ◽  
Synthetic Population ◽  
Household Travel

This paper describes a new method of population synthesis that includes land use information. The method is based on an initial identification of suitable land use summaries to build a spatial taxonomy at any spatial scale. This same taxonomy is then used to classify household travel survey records (persons and households) and in parallel geographic subdivisions for the state of California. This land use information is the added dimension in the population synthesis methods for travel demand analysis. Synthetic population generation proceeds by expanding (re-creating) the records of the households responding to the survey and the entire array of travel behavior data reproduced for the synthetic population. The basis for selecting the variables to use in the synthetic population is first testing their significance in simplified specification in models of travel behavior that include land use as an explanatory variable and account for the shape of behavioral data (e.g., observations with no travel). The paper shows differences between synthetic populations with and without land use data to demonstrate the behavioral realism added by this approach.

Analysis of the Effects of Connected–Automated Vehicle Technologies on Travel Demand

2017 ◽  
Vol 2625 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Joshua Auld ◽  
Vadim Sokolov ◽  
Thomas S. Stephens
Keyword(s):  
Traffic Flow ◽  
Travel Behavior ◽  
Energy Use ◽  
Travel Demand ◽  
Transportation System ◽  
Transportation Systems ◽  
Potential Range ◽  
Cruise Control ◽  
Automated Vehicle ◽  
Positive Effects

Connected–automated vehicle (CAV) technologies are likely to have significant effects not only on how vehicles operate in the transportation system, but also on how individuals behave and use their vehicles. While many CAV technologies—such as connected adaptive cruise control and ecosignals—have the potential to increase network throughput and efficiency, many of these same technologies have a secondary effect of reducing driver burden, which can drive changes in travel behavior. Such changes in travel behavior—in effect, lowering the cost of driving—have the potential to increase greatly the utilization of the transportation system with concurrent negative externalities, such as congestion, energy use, and emissions, working against the positive effects on the transportation system resulting from increased capacity. To date, few studies have analyzed the potential effects on CAV technologies from a systems perspective; studies often focus on gains and losses to an individual vehicle, at a single intersection, or along a corridor. However, travel demand and traffic flow constitute a complex, adaptive, nonlinear system. Therefore, in this study, an advanced transportation systems simulation model—POLARIS—was used. POLARIS includes cosimulation of travel behavior and traffic flow to study the potential effects of several CAV technologies at the regional level. Various technology penetration levels and changes in travel time sensitivity have been analyzed to determine a potential range of effects on vehicle miles traveled from various CAV technologies.

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