Zhong-Ren Peng

Trip-Based Optimal Power Management of Plug-in Hybrid Electric Vehicles

The plug-in hybrid electric vehicle (PHEV), utilizing more battery power, is considered a next-generation hybrid electric vehicles with great promise of higher fuel economy. The charge-depletion mode is more appropriate for the power management of PHEV, i.e. the state of charge (SOC) is expected to drop to a low threshold when the vehicle reaches the destination of the trip. Global optimization charge-depletion power management would be desirable. However, this has so far been hampered due the a priori nature of the trip information and the almost prohibitive computational cost of global optimization techniques such as dynamic programming (DP). This situation can be changed by the current advancement of Intelligent Transportation Systems (ITS) based on the use of on-board GPS, GIS, real-time and historical traffic flow data and advanced traffic flow modeling techniques. In this paper, gas-kinetic base trip modeling approach was used for the highway portion trip and for the local road portion the traffic light sequences throughout the trip will be synchronized with the vehicle operation. Several trip models approaches were studied for a specific case. The simulation results demonstrated significant improvement in fuel economy using DP based charge-depletion control compared to rule based control. The gas-kinetic based trip model for the highway portion can describe the dynamics of the traffic flow on highway with on/off ramps which may be missed by the model which used only the main road detectors data.

Urban Transport Trends and Policies in China and India: Impacts of Rapid Economic Growth

Abstract This paper provides a comparative overview of urban transport in the world’s two most populous countries: China and India. Cities in both countries are suffering from severe and worsening transport problems: air pollution, noise, traffic injuries and fatalities, congestion, parking shortages, energy use, and a lack of mobility for the poor. The urban transport crisis in China and India results from continuing population growth, urbanization, suburban sprawl, rising incomes, and skyrocketing motor vehicle ownership and use. This paper critically assesses government policies in each country and suggests a range of specific improvements. It advocates a slowdown in the massive roadway investment in recent years and a shift in emphasis to expanding and improving public transport, cycling, and walking facilities. While continued growth in motor vehicle use is inevitable, China and India should restrict motor vehicle use in congested city centres and increase taxes, fees, and charges to reflect the enormous social and environmental costs of motor vehicle use. At the same time, much stricter regulations should be imposed on manufacturers to produce cleaner, more energy‐efficient, quieter, and safer cars, motorcycles, buses, and trucks. Mitigating the many social and environmental impacts of rising motorization is obviously important for the future well‐being of Chinese and Indian cities. It is also crucial for the future of the rest of the world. Unless the problems of motorization in China and India can be effectively dealt with, the world faces sharp increases in greenhouse gases, accelerating climate change, and rapid depletion of a range of non‐renewable resources.

Internet GIS for Public Participation

Internet GIS, serving spatial data and GIS functionality on the web, offers a special and potentially important means to facilitate public participation in the planning and decision-making process. In this paper I discuss a framework for the design of a web-based public participation system (WPPS) that integrates Internet GIS, Internet communications, and scenario-building tools. The design framework is based on a taxonomy that is created to describe the level of services in serving public participation according to the information content, level of user interactivity, and system functionality. The system is designed to enhance public participation in the planning and decision-making process by providing the general public with data, analysis tools, and a forum to explore knowledge, express opinion, and discuss issues. The unique feature of the WPPS using Internet GIS is that it provides users not only the option of evaluating, commenting, and selecting alternatives, but also the capability of forming their own alternatives. I also discuss system components and design issues in the WPPS.

The roles of geography markup language (GML), scalable vector graphics (SVG), and Web feature service (WFS) specifications in the development of Internet geographic information systems (GIS)

Abstract.The objective of this paper is to address two issues of current Internet Geographic Information Systems (GIS) programs – interoperability and graphic image output issues – using standard-based technologies, specifically, the Geography Markup Language (GML), Scalable Vector Graphics (SVG) and the OpenGIS Web Feature Service (WFS) Implementation Specifications developed by the OpenGIS Consortium (OGC). A strategy is proposed to use GML as a coding and data transporting mechanism to achieve data interoperability, SVG to display GML data on the Web, and WFS as a data query mechanism to access and retrieve data at the feature level in real time on the Web. Two case studies are reported to implement this strategy. Our case studies show that the combination of GML, SVG, and WFS has an immense potential to achieve interoperability while not requiring considerable changes to existing legacy data. Data can be in their original formats and still be retrieved using WFS and transformed into GML in real time. SVG can produce superior quality vector maps on a Web browser. More research is needed to explore the full potential of these new standards and to test them in real-world situations.

Design and development of interactive trip planning for web-based transit information systems

This article presents a Web-based transit information system design that uses Internet Geographic Information Systems (GIS) technologies to integrate Web serving, GIS processing, network analysis and database management. A path finding algorithm for transit network is proposed to handle the special characteristics of transit networks, e.g., time-dependent services, common bus lines on the same street, and non-symmetric routing with respect to an origin/destination pair. The algorithm takes into account the overall level of services and service schedule on a route to determine the shortest path and transfer points. A framework is created to categorize the development of transit information systems on the basis of content and functionality, from simple static schedule display to more sophisticated real time transit information systems. A unique feature of the reported Web-based transit information system is the Internet-GIS based system with an interactive map interface. This enables the user to interact with information on transit routes, schedules, and trip itinerary planning. Some map rendering, querying, and network analysis functions are also provided.

Using Global Positioning System data to understand variations in path choice

A comprehensive set of Global Positioning System (GPS) vehicle location data from Lexington, Kentucky, households was analyzed to determine if such data can be helpful in improving path choice assumptions in traffic assignment models. The portion of the data used consisted primarily of a reconstruction of the street network and the lists of street segments in each path. Analysis was based on matches of trips (e.g., pairs of trips with similar origins and destinations). Matches were obtained for trips within households and for trips across households. Statistics used to compare trips in matches were a path deviation index and the percentage of identical links. It was found that the path chosen on a trip was quite sensitive to the location of the origin and destination and that the chosen path most often differed considerably from the shortest time path across the network. Paths for trips made by the same driver were very consistent over time; paths by different drivers showed more deviations even when the trip ends were the same or very similar. As a result of this research, recommendations are made as to how GPS data on path choice can be better collected in the future for improvement of traffic assignment models.

Optimal power management of plug-in HEV with intelligent transportation system

Hybrid electric vehicles (HEV) have demonstrated their capability of improving the fuel economy and emission. The plug-in HEV (PHEV), utilizing more battery power, has become a more attractive upgrade of HEV. The charge-depletion mode is more appropriate for the power management of PHEV, i.e. the state of charge (SOC) is expected to drop to a low threshold when the vehicle reaches the destination of the trip. In the past, the trip information has been considered as future information for vehicle operation and thus unavailable a priori. This situation can be changed by the current advancement of intelligent transportation systems (ITS) based on the use of on-board geographical information systems (GIS), global positioning systems (GPS) and advanced traffic flow modeling techniques. In this paper, a new approach of optimal power management of PHEV in the charge-depletion mode is proposed with driving cycle modeling based on the historic traffic information. A dynamic programming (DP) algorithm is applied to reinforce the charge-depletion control such that the SOC drops to a specific terminal value at the final time of the cycle. The vehicle model was based on a hybrid SUV. Only fuel consumption is considered for the current stage of study. Simulation results showed significant improvement in fuel economy compared with rule-based power management. Furthermore, simulations on several driving cycles using the proposed method showed much better consistency in fuel economy compared to the rule-based control.

Power management of plug-in hybrid electric vehicles using neural network based trip modeling

The plug-in hybrid electric vehicles (PHEV), utilizing more battery power, has become a next-generation HEV with great promise of higher fuel economy. Global optimization charge-depletion power management would be desirable. This has so far been hampered due to the a priori nature of the trip information and the almost prohibitive computational cost of global optimization techniques such as dynamic programming (DP). Combined with the Intelligent Transportation Systems (ITS), our previous work developed a two-scale dynamic programming approach as a nearly globally optimized charge-depletion strategy for PHEV power management. Trip model is obtained via GPS, GIS, real-time and historical traffic flow data and advanced traffic flow modeling. The Gas-kinetic based model was used for the trip modeling in our previous study. The complicated partial deferential equation based model with several parameters needs to be calibrated had for implementation. In this paper, a neural network based trip model was developed for the highway portion, using the given data from WisTransPortal. The real test data was used for the training and validation of the network. The simulation results show that the obtained trip model using neural network can greatly improve the trip modeling accuracy, and thus improve the fuel economy. The potential of the advantages were indicated by the fuel economy comparison.

Motorist Response to Arterial Variable Message Signs

Although much research into driver attitudes toward and responses to expressway-based variable message signs (VMSs) has been conducted, little research has explored motorist responses to VMSs on arterial surface streets. Arterial VMSs, located near main expressway entrance points, may prove to be more effective at inducing motorists to divert during congestion or incidents than their highway counterparts because they will allow drivers to have many more routing options before they commit to the expressway system. The results of a study conducted in Milwaukee, Wisconsin, to discern motorist attitudes toward arterial VMSs and the diversion behaviors induced by those signs are presented. A revealed-preference survey conducted in the immediate vicinity of the arterial VMS found that nearly two-thirds of the respondents obtained traffic information from the signs more than once per week. Furthermore, 66% of those surveyed changed their route at least once per month because of the information received from the arterial VMS. These results demonstrate that motorists are responsive to messages on arterial VMSs; arterial VMSs have an important impact on motorist’s travel behavior and are thus a key component in a comprehensive traffic management system. More specifically, an ordered logit model revealed that the propensity to divert was correlated to the frequency that a driver encounters an arterial VMS, motorists’ perception of the VMS information as useful, and motorists’ trust in the accuracy of the VMS information. The reasons for this diversion response are primarily to save travel time and to avoid expressway incidents.

Trip Based Power Management of Plug-in Hybrid Electric Vehicle with Two-Scale Dynamic Programming

The plug-in HEV (PHEV), utilizing more battery power, has become the next-generation HEV with great promise of higher fuel economy. The charge-depletion mode is more appropriate for the power management of PHEV, i.e. the state of charge (SOC) is expected to drop to a low threshold when the vehicle reaches the destination of the trip. Globally optimized charge-depletion power management would be desirable. However, this has so far been hampered due to the a priori nature of the trip information and the prohibitive computational cost of global optimization techniques such as dynamic programming (DP). This situation can be changed by the current advancement of intelligent transportation systems (ITS) based on the use of on-board GPS, GIS, real-time and historical traffic flow data and advanced traffic flow modeling techniques. In this paper, charge-depletion control of PHEV is nearly globally optimized with a two-scale dynamic programming approach based on trip modeling with real-time and historical traffic data. For DP based charge-depletion control of PHEV, the SOC is desired to drop to a specific terminal value at the end of the trip. By specifying the origin and destination of a trip, the trip model, i.e. the driving cycle, is first obtained with the average of the historic traffic data, and the globally optimized SOC profile can be obtained by solving the overall or the macro-scale DP problem. The actual power management can be adapted during real-time vehicle operation with a micro-scale DP framework. The whole trip is divided into a number of segments, and for each segment, a smaller DP will be solved using the on-line traffic data transmitted to the vehicle from the traffic flow sensors within the segment. The SOC obtained in the macro-scale DP solution at the terminal location is reinforced to be the final value. Simulation study has been performed on a hybrid SUV model from ADVISOR, and a defined trip in the greater Milwaukee area. The simulation results demonstrated significant improvement in fuel economy using DP based charge-depletion control compared to rule based control, and also the benefit of adaptation using the two-scale DP method.