Daniel Sun

Research and Implementation of Lane-Changing Model Based on Driver Behavior

Lane-changing algorithms have attracted increased attention during recent years. However, limited research has been conducted to address the probability of changing lanes and vehicle interactions that occur. The objective of this study is to model urban lane-changing maneuvers by using data related to driver behavior. Two components were developed from field data, for lane-changing probability and for gap acceptance. Two experiments were conducted to collect the corresponding lane-changing data: a focus group study and an in-vehicle driving test. The proposed lane-changing model was implemented in the CORSIM microscopic simulator. Traffic data collected from a busy arterial street were used for model calibration and validation, and the simulation capabilities of the newly developed model were compared with the original lane-changing model embedded in CORSIM. Results indicate that the new model replicates observed traffic under different levels of congestion better than the original model does.

Lane-changing behavior on urban streets: a focus group-based study.

As lane-changing behavior has received increasing attention during the recent years, various algorithms have been developed. However, most of these models were derived and validated using data such as vehicle trajectories, with no consideration of driver characteristics. In this research, focus group studies were conducted to obtain driver-related information so that the driver characteristics can be incorporated into lane-changing models. Different urban lane-changing scenarios were examined and discussed in the focus group meetings. The likelihood for initiating lane changes under each scenario was obtained. The participating drivers were categorized according to their background information and verbal responses, so that the lane-changing behavior can be related to driver characteristics for each group. Two types of information, quantitative and qualitative responses from participants, were used to establish this relationship. The paper concludes by providing recommendations related to the implementation of study findings into micro-simulators to better replicate driver behavior in urban street networks.

Urban travel behavior analyses and route prediction based on floating car data

Abstract The lack of sufficient data is the result of the inherent complexity of gathering and subsequently analyzing route choice behavior, which unfortunately hasn’t been revealed much by existing literatures. With the assistance of GIS technology and taxi-based floating car data, the authors found that the majority of urban drivers would not travel along the shortest or the fastest paths. This paper studies the factors that influence commuters’ route choice and route switching based on objective real-world observations of travel behavior. Possible factors that may affect driver’s route choice are then analyzed and regression methods were introduced to attain if there existing a clear quantitative relationship between drivers’ route choice and these factors. The result indicates that such connection is difficult to be established. Consequently, eight scenarios were proposed to quantify the influence of various potential factors. Analysis shows that travel distance, travel time and road preference have comparable higher influence on drivers’ route choice. To this end, a new route prediction model is proposed, adopting the road usage as the weight and the shortest route’s length and the fastest route’s time as the constraints. The proposed model was implemented and validated using the FCD data of Shenzhen, China. The results indicate that by combining the external influence with the driver’s personal preference, the predicted travel route has a higher matching ratio with the actual one, which consequently indicates the effectiveness of the model.

Comparative study on simulation performances of CORSIM and VISSIM for urban street network

Abstract With the progress of simulation technologies, many transportation simulation packages were developed. However, little information is available to the users in applying these models to the most appropriate situations, or even seldom with the simulation accuracy of the individual model. This study conducts a comparative analysis of two popular simulation models (VISSIM and CORSIM), based on their simulation performances on an urban transportation network. Road network and field traffic data from North Bund, Hongkou District, Shanghai, China were used as the simulation background and input. Sensitivity analysis was carried out to compare the performance of both models based on four key indices, namely software usability, average control delay, average queuing length, and cross-sectional traffic volume. Advantages of each simulator were identified based on comparison analyses of simulations with different levels of congestion and intersection geospatial scales. The main performance difference was found lying in the default parameter configuration within the models, including driver behavior settings, traffic environment settings, and vehicle types, etc. Consequently, it was recommended that analysts should choose their appropriate tools based on intersection type and level of saturation within the simulation case.

Short-Term Bus Passenger Demand Prediction Based on Time Series Model and Interactive Multiple Model Approach

Although bus passenger demand prediction has attracted increased attention during recent years, limited research has been conducted in the context of short-term passenger demand forecasting. This paper proposes an interactive multiple model (IMM) filter algorithm-based model to predict short-term passenger demand. After aggregated in 15u2009min interval, passenger demand data collected from a busy bus route over four months were used to generate time series. Considering that passenger demand exhibits various characteristics in different time scales, three time series were developed, named weekly, daily, and 15u2009min time series. After the correlation, periodicity, and stationarity analyses, time series models were constructed. Particularly, the heteroscedasticity of time series was explored to achieve better prediction performance. Finally, IMM filter algorithm was applied to combine individual forecasting models with dynamically predicted passenger demand for next interval. Different error indices were adopted for the analyses of individual and hybrid models. The performance comparison indicates that hybrid model forecasts are superior to individual ones in accuracy. Findings of this study are of theoretical and practical significance in bus scheduling.

Development of Web-Based Transit Trip-Planning System Based on Service-Oriented Architecture

The majority of transit trip planners exist as proprietary systems based on particular vendor products. With the incorporation of more functional components, system maintenance and regular transit information updates become burdensome tasks for transit agencies. In addition, the proprietary nature of the systems makes it difficult to take advantage of the rapid advancement of geospatial information and web technologies. The authors proposed an open and interoperable transit trip-planning system based on a service-oriented architecture, with the principle of reusing the existing modular resources, while providing user-friendly interfaces for expansion of functionality. The objective was to integrate geospatial services available online (such as Google Maps), open-source geospatial database technologies, and path-finding algorithms in a loosely coupled manner. The proposed system was developed with spatial and temporal transit data from Waukesha Metro Transit in Wisconsin. Research results were validated by comparing outputs from the existing South-East Wisconsin Transit Trip Planner and route schedule matching. Comparison results showed that the new service-oriented architecture provided a flexible, efficient mechanism for transit-trip planners. The architecture took advantage of rapidly changing online geospatial services, yet maintained the core functions of itinerary search that may be unique to each transit agency.

Optimize the Settings of Variable Speed Limit System to Improve the Performance of Freeway Traffic

This paper investigates variable speed limit (VSL) systems, trying to optimize the system designs when the variable message signs (VMSs) are movable. The optimization problem is formulated as a large mixed-integer nonlinear programming problem, whose decision variables include the number of VMSs to be deployed, the locations of the VMSs, and the speed limits posted on the VMSs. Two objectives are considered, one is to smooth the flow propagation, and the other is to minimize the environmental impact of freeway traffic. Moreover, a genetic algorithm is proposed to solve the complex problem. Numerical examples performed on a real freeway segment show that VSL can effectively achieve smooth flow and reduce the environmental impact of freeway traffic.

A bus route evaluation model based on GIS and super-efficient data envelopment analysis

ABSTRACT When compared to large cities in developed countries, the shares of public transportation in most Chinese cities are low. Increasing the competitiveness of urban public transportation remains an urgent problem. A capable evaluation method for public transportation is required to assist the development of urban transit systems. This paper focuses on the bus system. Being devoid of standard criteria, it is difficult to determine the efficiency of a transit system or any bus line using a single evaluation index. This paper proposes a comparative analysis to evaluate bus lines so as to filter out candidates for further optimization. From the viewpoints of transit planning, operation and quality of service, this paper establishes 10 subordinate evaluation indices and then uses geographical information system tools, global positioning system data and smart card data to assist the index definition and calculation. Super-efficient data envelopment analysis (DEA) method is adopted for the proposed single factor and comprehensive evaluation models. Finally, the bus system in Shenzhen, China is used as a case study. The comparable significant results validate the capability of the proposed model.

Traffic Congestion Evaluation Method for Urban Arterials: Case Study of Changzhou, China

Quantitative analysis of traffic conditions provides a benchmark for evaluating traffic states for effective operation and management. Most existing studies focus on measuring congestion in freeway continuous traffic flow. This paper quantifies states of urban interrupted traffic flow by using field traffic data from arterial roads in Changzhou, Jiangsu Province, China, as a study case. Development of the average congestion index considered traffic volumes on various segments to reflect the congestion state of the entire road. The congestion travel rate reflected the difference between the studied state and the free-flow state. The two indexes were adopted for measuring congestion quantitatively for both weekday (March 24, 2010) and weekend (March 28, 2010) traffic. In addition, the fuzzy clustering method was used to obtain threshold values for various traffic states, and three states were proposed from the empirical study on traffic conditions in Changzhou. Under this classification, congestion quantifications of field-observed trends from both the weekday and the weekend were found to be consistent with the definitions of the Urban Traffic Management Evaluation System of China. This finding further validated the effectiveness of the proposed average congestion index indicator.

Rule-Based Forecasting of Traffic Flow for Large-Scale Road Networks

As traffic data collection becomes less costly and more commonplace, large-scale traffic flow forecasting is increasingly needed. This paper proposes a rule-based approach for forecasting traffic flow based on the K nearest neighbor (KNN) nonparametric regression model, the rule-based KNN (RKNN) model. Rules were extracted from the historical data through the use of rough set theory, which found the nearest neighbors. Traffic impact factors, such as weather and time of day, were incorporated into the rules. Every historical record was labeled with a rule. With current data on traffic flow states and traffic flow impact, the nearest neighbors could be found quickly from the historical data records covered by the corresponding rule. An additional methodology was proposed to keep the historical data and the rules up to date. A case study on an Interstate freeway in Virginia, I-395, was conducted to evaluate the performance of the RKNN approach. The results showed that the proposed approach could decrease the mean absolute percentage error by 26.86%. Moreover, the proposed algorithm reduced calculation time by 65.69%, compared with the traditional KNN algorithms. This difference indicates the effectiveness of the proposed algorithm for use with large urban road networks.