Modeling and evaluation of the impact of motorcycles mobility on vehicular traffic

Traffic simulation can help to evaluate the impact of different mobility behaviors on the traffic flow from safety, efficiency, and environmental views. The objective of this paper is to extend the SUMO (Simulation of Urban Mobility) road traffic simulator to model and evaluate the impact of motorcycles mobility on vehicular traffic. First, we go through diverse mobility aspects and models for motorcycles in SUMO. Later, we opt for the most suitable mobility models of motorcycles. Finally, the impact of motorcycle mobility on different kinds of vehicles is investigated in terms of environment, fuel consumption, velocity and travel time. The result of modeling and evaluation shows that based on the mobility model of the motorcycle, vehicular traffic flow can be enhanced or deteriorated

A Review of the Travel Behavior Analysis: Its Basis and Application for Developing Cities

Travel, most often viewed in theory as derived from the demand for activity participation, has almost always been modeled on the trip-based basis, i.e., the trip is treated as the unit of analysis. Attributes of a trip (e.g., its origin and destination, mode, length) have been the subjects of analysis, but not the types of activities engaged in, their durations, sequences, and timing. This paper offers a brief review of the travel behavior analysis in order to provide a better understanding and forecasting of travel behavior. The article further offers discussions on its possible applications in urban areas of developing countries where historical accumulations of transportation and communications technologies are being introduced within a short span of time, creating the environment for travel which may not be properly accounted for using the conventional trip-based models of travel demand. In addition, the dataset from Malaysia was employed as a case of study

Procedural Modeling and Physically Based Rendering for Synthetic Data Generation in Automotive Applications

We present an overview and evaluation of a new, systematic approach for generation of highly realistic, annotated synthetic data for training of deep neural networks in computer vision tasks. The main contribution is a procedural world modeling approach enabling high variability coupled with physically accurate image synthesis, and is a departure from the hand-modeled virtual worlds and approximate image synthesis methods used in real-time applications. The benefits of our approach include flexible, physically accurate and scalable image synthesis, implicit wide coverage of classes and features, and complete data introspection for annotations, which all contribute to quality and cost efficiency. To evaluate our approach and the efficacy of the resulting data, we use semantic segmentation for autonomous vehicles and robotic navigation as the main application, and we train multiple deep learning architectures using synthetic data with and without fine tuning on organic (i.e. real-world) data. The evaluation shows that our approach improves the neural network's performance and that even modest implementation efforts produce state-of-the-art results.Comment: The project web page at http://vcl.itn.liu.se/publications/2017/TKWU17/ contains a version of the paper with high-resolution images as well as additional materia

Motorcycle Movement Model Based on Markov Chain Process in Mixed Traffic

Mixed traffic systems are dynamically complex since there are many parameters and variables that influence the interactions between the different kinds of vehicles. Modeling the behavior of vehicles, especially motorcycle which has erratic behavior is still being developed continuously, especially in developing countries which have heterogeneous traffic. To get a better understanding of motorcycle behavior, one can look at maneuvers performed by drivers. In this research, we tried to build a model of motorcycle movement which only focused on maneuver action to avoid the obstacle along with the trajectories using a Markov Chain approach. In Markov Chain, the maneuver of motorcycle will described by state transition. The state transition model is depend on probability function which will use for determine what action will be executed next. The maneuver of motorcycle using Markov Chain model was validated by comparing the analytical result with the naturalistic data, with similarity is calculated using MSE. In order to know how good our proposed method can describe the maneuver of motorcycle, we try to compare the MSE of the trajectory based on Markov Chain model with those using polynomial approach. The MSE results showed the performance of Markov Chain Model give the smallest MSE which 0.7666 about 0.24 better than 4th order polynomial

System Dynamics Model of Shanghai Passenger Transportation Structure Evolution

AbstractBased on the data from a comprehensive transportation survey of Shanghai in 2004 and 2009, this paper analyzed the evolution of urban passenger transportation structure using the system dynamics approach. A system dynamics model of Shanghai passenger transportation structure evolution is proposed, which consists of setting modeling targets, establishing transportation system boundaries, causality analysis, establishing flow diagram, parameter estimation and model validation

Torque vectoring based drive assistance system for turning an electric narrow tilting vehicle

The increasing number of cars leads to traffic congestion and limits parking issue in urban area. The narrow tilting vehicles therefore can potentially become the next generation of city cars due to its narrow width. However, due to the difficulty in leaning a narrow tilting vehicle, a drive assistance strategy is required to maintain its roll stability during a turn. This article presents an effective approach using torque vectoring method to assist the rider in balancing the narrow tilting vehicles, thus reducing the counter-steering requirements. The proposed approach is designed as the combination of two torque controllers: steer angle–based torque vectoring controller and tilting compensator–based torque vectoring controller. The steer angle–based torque vectoring controller reduces the counter-steering process via adjusting the vectoring torque based on the steering angle from the rider. Meanwhile, the tilting compensator–based torque vectoring controller develops the steer angle–based torque vectoring with an additional tilting compensator to help balancing the leaning behaviour of narrow tilting vehicles. Numerical simulations with a number of case studies have been carried out to verify the performance of designed controllers. The results imply that the counter-steering process can be eliminated and the roll stability performance can be improved with the usage of the presented approach

The integrated dynamic land use and transport model MARS

Cities worldwide face problems like congestion or outward migration of businesses. The involved transport and land use interactions require innovative tools. The dynamic Land Use and Transport Interaction model MARS (Metropolitan Activity Relocation Simulator) is part of a structured decision making process. Cities are seen as self organizing systems. MARS uses Causal Loop Diagrams from Systems Dynamics to explain cause and effect relations. MARS has been benchmarked against other published models. A user friendly interface has been developed to support decision makers. Its usefulness was tested through workshops in Asia. This paper describes the basis, capabilities and uses of MARS

Toward Modeling Erosion on Unpaved Roads In Mountainous Northern Thailand

Contributions of road networks and unstable agricultural activities to downstream sedimentation, water shortages, and flooding in mainland SE Asia are not easily determined because scientific understanding of runoff and erosion processes operating on roads is limited. This dissertation work, conducted within the Pang Khum Experimental Watershed (PKEW) in northern Thailand, supports that owing to low saturated hydraulic conductivity (Ks < 1 6 mm h-1), Horton overland flow (HOF) generation occurs more frequently on unpaved PBCEW roads than on other watershed surfaces having higher infiltrability (e.g., mean Ks for agricultural surfaces ranges from 130 to 320 mm h-1)- Because of frequent HOF generation, the road system contributes to stream sedimentation throughout the rainy season. The highly compacted (bulk density = 1.45 Mg m-3) PKEW road surface typically underlies a layer of loose material of finite depth. Instantaneous sediment transport (St) on roads varies because the supply of easily transported surface sediment is constantly altered by overland flow events, traffic, road maintenance, and mass wasting events, both during and between storms. As surface material is removed during an overland flow event, normalized S( declines from an initial peak rate of ~ 3 g J-1 to a steady rate of =0.5 g J-1 The mechanical stress associated with vehicle passes during a storm increases the availability of loose material, producing 2-4 fold increases in St, and sediment concentration (Ct) values. Herein, rainfall simulation data, surveys of traffic phenomena, and soil property measurements were used to parameterize the physics-based KINER0S2 model for simulating road runoff and erosion. During model validation, instantaneous discharge was simulated well (root mean squared error (RMSE) = 14%). However, because KINEROS2 equations do not “describe” road erosion processes accurately, St was simulated poorly (RMSE = 51.6%). To improve modeling, a methodology recognizing the dynamic erodibility (DE) of a road surface was introduced. By explicitly simulating removal of a layer of loose material, the DE modeling technique improved prediction of St (RMSE decreased to 35.4 %). Finally, a systematic approach is presented to implement DE modeling on any road surface where baseline erodibility and sediment availability can be quantified