Wei Ming Ho

Development and Empirical Study of Real-Time Simulation-Based Dynamic Traffic Assignment Model

This research aims at developing a system of real-time simulation-based dynamic traffic model under mixed traffic flow conditions. The system consists of two layers, namely, simulation layer and real-time control layer. The system is implemented based on rolling horizon approach, which is advanced for each stage; thus real-time data can be incorporated within the framework. In order to predict normative, as well as predictive, information, a simulation-based dynamic traffic assignment model is employed within each stage. Empirical data for a real city network, such as flows from vehicle detectors, are used to validate the model in a real-time environment. The values of mean absolute percentage error and root-mean-squared percentage error are within 15%, and the results show promising agreements between observed and simulated flows.

Fuel consumption and emission models development and application for advanced traffic management strategies

Due to the development of industry and commerce, the percentage of fuel consumptions and emissions of the transport sector is increasing in recent years. As a result, the levels of CO2 and other emissions have worsened the environment, and thus global-warming and air-pollution issues need to be incorporated within the planning and operation of transportation system. This research proposes two energy consumption and emission models for mixed traffic flows under a wide variety of advanced traffic management strategies. These two models are defined as the link-based and the trip-based fuel consumption and emission model. The link-based model is developed based on link characteristics, including static and dynamic attributes of the link, such as link length, number of vehicles, and average speed on links. The trip-based model calculates fuel consumption and emission based on trip characteristics, such as vehicle movement trajectory. These two models are integrated with a simulation-assignment model, DynaTAIWAN. Numerical experiments are conducted to illustrate the proposed models. Traffic management strategies, including real-time information and advanced traffic control systems, are evaluated based on the performance of fuel consumption and emission. The experiment results show the models are robust and advanced traffic management strategies can indeed reduce fuel consumption and CO2 emission.

Impact assessment of network reliability with route information under severe weather

Climate change and severe weather impacts on transportation and infrastructure have become global and environmental issues in the whole world. All human and transportation infrastructure systems are affected by climate change. Taiwan was hit by Typhoon Morakot and it caused a flood disaster in August 2009. This research focuses on the weather assessment measures of network reliability under route information. Two indexes are proposed to examine network reliability under route information, including: connectivity reliability and travel time reliability. Numerical experiments are conducted based on a real traffic network, the Jiaxian network, to illustrate the impact assessment of network reliability.

A modified entropy-based dynamic gravity model for the evacuation trip distribution problem during typhoons

Abstract Typhoon Morakot, which formed on 2 August 2009, was the deadliest typhoon in Taiwan’s history, responsible for over 700 deaths on the island. During the typhoon evacuation process, one critical issue is how to efficiently distribute the evacuation trips to a limited number of shelters based on both spatial and temporal considerations. This paper proposes a modified entropy-based dynamic gravity model to reflect the spatial and temporal distribution of the evacuees and the shelters. A unique feature of the proposed model is that the entropy is explicitly incorporated within the travel cost constraints. The spatial and temporal relationships between evacuees and shelters can be reflected through the impedance functions and the discretized time intervals with better performance than the traditional model. A simulation-assignment model is applied to generate the zone-to-zone travel time. A calibration analysis based on the solution procedure is conducted for the Jiasian network, in Kaohsiung city, which was heavily affected by the Typhoon Morakot. The calibration results show that the modified entropy-based dynamic gravity model leads to better convergence patterns in the entropy values, higher travel cost coefficients, and lower average generalized trip costs than the traditional model, and is suitable for use with the evacuation plan during typhoons.

Prediction of the Impact of Typhoons on Transportation Networks with Support Vector Regression

AbstractThe ability to predict the impact of typhoons on transportation infrastructure is important as it can help to avoid serious delays and dangers when roads are closed due to such events. This research uses support vector regression (SVR) to predict the impact of typhoons on transportation infrastructure. It first integrates and examines the infrastructure and precipitation data from different authorities. An SVR model is constructed to solve a nonlinear prediction problem for small size data. The SVR model is calibrated and validated by a heuristic process. The calibrated and validated results are then applied to predict closed roads in a real network through a simulation assignment model. Several traffic management strategies are developed to reduce the negative impacts of typhoons. The results show that the mean absolute percentage error (MAPE) of SVR prediction is 9.7%. The impact of typhoons on transportation networks can thus be predicted and simulated based on the calibrated SVR model, and app...

Simulation-based travel time prediction model for traffic corridors

This research aims at constructing a simulation-based travel time prediction model for traffic corridors. The simulation-assignment model, DynaTAIWAN is utilized to predict the travel time based on two approaches, a flow-based model and a vehicle-based model. Dynamic origin-destination (O-D) estimation and prediction procedure is developed to prepare O-D demand data, and the estimated O-D flows is used within DynaTAIWAN to simulate vehicle movements. The developed framework is illustrated for a traffic corridor and validated through empirical data. Empirical data for signalized urban network, the travel time from electronic toll stations are used to validate the model.