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 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 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.

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 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.

Map-Matching Algorithm for Applications in Multimodal Transportation Network Modeling

2015 ◽  
Vol 2537 (1) ◽  
pp. 62-70 ◽  
Author(s):  
Kenneth Perrine ◽  
Alireza Khani ◽  
Natalia Ruiz-Juri
Keyword(s):  
Public Transportation ◽  
Transportation Network ◽  
Data Representation ◽  
Transportation Systems ◽  
Data Sets ◽  
The Public ◽  
Standard Data ◽  
Planning Models ◽  
Transit Agencies ◽  
Public Transportation Systems

Generalized Transit Feed Specification (GTFS) files have gained wide acceptance by transit agencies, which now provide them for most major metropolitan areas. The public availability GTFSs combined with the convenience of presenting a standard data representation has promoted the development of numerous applications for their use. Whereas most of these tools are focused on the analysis and utilization of public transportation systems, GTFS data sets are also extremely relevant for the development of multimodal planning models. The use of GTFS data for integrated modeling requires creating a graph of the public transportation network that is consistent with the roadway network. The former is not trivial, given limitations of networks often used for regional planning models and the complexity of the roadway system. A proposed open-source algorithm matches GTFS geographic information to existing planning networks and is also relevant for real-time in-field applications. The methodology is based on maintaining a set of candidate paths connecting successive geographic points. Examples of implementations using traditional planning networks and a network built from crowdsourced OpenStreetMap data are presented. The versatility of the methodology is also demonstrated by using it for matching GPS points from a navigation system. Experimental results suggest that this approach is highly successful even when the underlying roadway network is not complete. The proposed methodology is a promising step toward using novel and inexpensive data sources to facilitate and eventually transform the way that transportation models are built and validated.

Analysis of Dynamic Impedance Functions in Urban Traffic Flow Guidance System

2012 ◽  
Vol 238 ◽  
pp. 503-506 ◽  
Author(s):  
Zhi Cheng Li
Keyword(s):  
Traffic Flow ◽  
Network Model ◽  
Large Scale ◽  
Choice Model ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Guidance System ◽  
Traffic Network ◽  
Dynamic Traffic ◽  
Run Time

The successful application of Intelligent Transportation Systems (ITS) depends on the traffic flow at any time with high-precision and large-scale assessments, it is necessary to create a dynamic traffic network model to evaluate and forecast traffic. Dynamic route choice model sections of the run-time function are very important to the dynamic traffic network model. To simplify the dynamic traffic modeling, improve the calculation accuracy and save computation time, the flow on the section of the interrelationship between the exit flow and number of vehicles are analyzed, a run-time functions into the flow using only sections of the said sections are established.

Incorporating Trip-Chaining Behavior into Network Equilibrium Analysis

2005 ◽  
Vol 1921 (1) ◽  
pp. 11-18 ◽  
Author(s):  
Takuya Maruyama ◽  
Noboru Harata
Keyword(s):  
Large Scale ◽  
Travel Demand ◽  
Demand Forecasting ◽  
Equilibrium Analysis ◽  
Network Equilibrium ◽  
Promising Alternative ◽  
Second Best ◽  
Trip Chaining ◽  
Large Scale Networks ◽  
Almost All

The network equilibrium model is a useful tool for long-term transportation planning and is one promising alternative to the traditional four-step travel forecasting model. However, some issues with the model remain to be considered. For example, almost all variations of the model adhere to the traditional trip-based approach, in which trip chains made by users are treated as separate, independent entities in the analysis. This research aims to develop a simple, tractable model to overcome this problem. One proposed model is based on piston-type trip chaining, and another accommodates any other type of trip chaining and includes congestion phenomena. These proposed models have certain key features: they have been successfully formulated as convex minimization problems, so uniqueness and algorithm convergence are easily proved; traveler behavior is based on theoretically sound random utility models, which allows the benefit of transportation projects to be calculated such that it is consistent with travel demand forecasting; and optimal road pricing can be calculated even in large-scale networks. These models are examined with the use of simple network examples, with special attention paid to the effect of trip-chaining behavior at the level of second-best toll. In a simple two-destination network, the second-best toll of the trip-based model is lower than that of trip chain–based model, indicating one of the biases of the trip-based model.

Estimating Path Travel Costs for Heterogeneous Users on Large-Scale Networks: Heuristic Approach to Integrated Activity-Based Model–Dynamic Traffic Assignment Models

2017 ◽  
Vol 2667 (1) ◽  
pp. 119-130 ◽  
Author(s):  
Xiang (Alex) Xu ◽  
Fatemeh Fakhrmoosavi ◽  
Ali Zockaie ◽  
Hani S. Mahmassani
Keyword(s):  
Large Scale ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Heuristic Approach ◽  
Dynamic Traffic ◽  
Travel Costs ◽  
Vehicle Trajectories ◽  
Optimal Paths ◽  
Large Scale Networks ◽  
Generalized Cost

Integrating activity-based models (ABMs) with simulation-based dynamic traffic assignment (DTA) have gained attention from transportation planning agencies seeking tools to address the arising planning challenges as well as transportation policies such as road pricing. Optimal paths with least generalized cost are needed to route travelers at the DTA level, while at the ABM level, only the least generalized cost information is needed (without fully specified paths). Thus, rerunning (executing) the least generalized cost path-finding algorithm at each iteration of ABM and DTA does not seem to be efficient, especially for large-scale networks. Furthermore, storing the dynamic travel cost skims for multiclass users as an alternative approach is not efficient either in regard to memory requirements. In this study, the aim was to estimate the least generalized cost so as to be used in destination and mode choice models at the ABM level. A heuristic approach was developed to use the simulated vehicle trajectories that were assigned to the optimal paths in the DTA level to estimate different cost measures, including distance, time, and monetary cost associated with the least generalized cost path for any given combination of the origin, destination, and departure time (ODT) and value of time. The proposed approximation method presented in this study used vehicle trajectories, aligned with the origin–destination direction and located in a specific boundary shaping an ellipse around the origin and destination zones at a certain time window, to estimate travel costs for the given ODT and user class. Numerical results for two real-world networks suggest the applicability of the method in large-scale networks in addition to its lower computational burden, including solution time and memory requirements, relative to other alternative approaches.

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