Myungsik Do

Correlation between optimal carsharing locations and carbon dioxide emissions in urban areas

Carsharing, an alternative to car ownership, is being encouraged by many national governments as a means to alleviate air pollution and traffic congestion. Previously, many carsharing companies determined service locations through trial and error, but they currently define their parking locations in metropolitan cities for maximum customer coverage. However, identifying carsharing locations according to the experiences of the pioneering cities might not yield valid results in some Asian countries where carsharing systems are unknown. Hence, this study examines the characteristics of carsharing users in Daejeon, a small Korean city, to determine that city’s optimal carsharing service locations. A geographic information system was used to analyze and determine the best spatial areas according to two data categories: internal and external demand factors. Suitable carsharing locations were ranked by the results of a grid analysis. Thirty optimal locations were then determined from the location-allocation model in a network analysis module. Determining optimal carsharing locations should also be directly correlated with the reduction of carbon dioxide emissions. Carbon dioxide emission reduction from carsharing was predicted at 62,070 tCO2eq for the year 2013; emission reductions were predicted to increase further to 172,923 tCO2eq by 2020. Thus, carsharing is an innovative strategy for traffic demand management that can alleviate air pollution. The results of this study indicate that further research is necessary to examine the relationship between optimal carsharing locations and carbon dioxide emission reduction from using lower-emission carsharing vehicles, such as electric vehicles.

A systematic methodology for testing the non-neutrality hypothesis of traffic information

This paper presents a systematic methodology for testing the neutrality hypothesis of traffic information based on in-house experiments. Traffic information is called ‘neutral’ if it does not convey any more information than each driver already has. Traffic information must be nonneutral toward the drivers learning behavior so that the traffic information system may have an effect on route guidance over the long term. Thus, our main interest is whether traffic messages have informational powers in differentiating rational expectations. The hypothesis is formulated in the form of a combined hypothesis, which can be effectively investigated by multiple hypothesis testing. The hypothesis requires that traffic information convey no information when drivers behave with rational expectations. This paper concludes that, as far as our experiments are concerned, the hypothesis can be rejected; traffic information may have informational powers even over the long term.

Analysis on safety impact of red light cameras using the Empirical Bayesian approach

Traffic signal violations at urban intersections have become a major safety concern in urban areas since, compared to other types of crashes, they may cause a higher percentage of injuries and fatal crashes. Therefore, the deployment of red light cameras has been concentrated on urban intersections, both to prevent traffic signal violations and to reduce vehicle crashes. Other methodologies for establishing the safety impact of red light cameras are also being researched, but accurate results are difficult to ascertain. In this study, the safety impact of red light cameras on urban intersections in Daejeon Metropolitan City, South Korea was assessed using the Empirical Bayesian (EB) approach. According to the EB analysis in this study, red light camera has increased fatal crashes in all crash types by around 2%. However, it has also increased injury crashes in all crash types by about 53%. In specific types of crashes at urban intersections, such as angle, sideswipe & rear-end, and head-on, red light cameras have reduced fatal crashes by around 23%. However, injury crashes for typical crash types at urban intersections have actually increased by around 37%. Furthermore, the EB analysis results of total crashes are similar to those of injury crashes. This may be the result of a higher than average travel speed on arterial link sections in Daejeon compared to that of other metropolitan cities like Seoul. Therefore, the higher average speed on arterial roads may be the cause of the increase of injuries resulting from sideswipe & rear-end crashes.

Comparative analysis of informational evacuation guidance by lane-based routing

ABSTRACT Road networks play a crucial role in modern society, particularly in the case of disasters or in emergency situations. Therefore, urban evacuation roads need to be selected and maintained in order to deal with potential disaster situations. In this study, we suggest methods of selecting emergency evacuation roads, with consideration to performance on linkage and connectivity of road networks in urban areas. Furthermore, we analyse the effects of evacuation guidance information by predetermined evacuation roads and lane-based routing methods. Through network analysis, we have performed quantitative analysis of road networks for a selection of emergency evacuation roads, based on information from the geographic information system (GIS) database. The distribution pattern of traffic volume and the impact of network nodes were also examined. This examination was carried out by using methods of network analysis such as the degree and closeness centralities. The travel pattern and travel time cost of users in disaster situations are analysed through microscopic traffic simulations. By comparing situations before and after roads are disconnected, the impact and effect of each link can be calculated. Total travel time, travel distance and delay time for all evacuees are used to measure each scenario. We found that, through selected evacuation roads, the evacuation time-shortening effect is significant when compared to that of the operation of existing traffic signal systems. Also, the evacuation time-shortening effect is greater when additionally applied to the lane-based routing method on emergency evacuation roads.

Road Maintenance Planning with Traffic Demand Forecasting

PURPOSES : This study aims to examine the differences between the existing traffic demand forecasting method and the traffic demand forecasting method considering future regional development plans and new road construction and expansion plans using a four-step traffic demand forecast for a more objective and sophisticated national highway maintenance. This study ultimately aims to present future pavement deterioration and budget forecasting planning based on the examination. METHODS : This study used the latest data offered by the Korea Transport Data Base (KTDB) as the basic data for demand forecast. The analysis scope was set using the Daejeon Metropolitan City`s O/D and network data. This study used a traffic demand program called TransCad, and performed a traffic assignment by vehicle type through the application of a user equilibrium-based multi-class assignment technique. This study forecasted future traffic demand by verifying whether or not a realistic traffic pattern was expressed similarly by undertaking a calibration process. This study performed a life cycle cost analysis based on traffic using the forecasted future demand or existing past pattern, or by assuming the constant traffic demand. The maintenance criteria were decided according to equivalent single axle loads (ESAL). The maintenance period in the concerned section was calculated in this study. This study also computed the maintenance costs using a construction method by applying the maintenance criteria considering the ESAL. The road user costs were calculated by using the user cost calculation logic applied to the Korean Pavement Management System, which is the existing study outcome. RESULTS : This study ascertained that the increase and decrease of traffic occurred in the concerned section according to the future development plans. Furthermore, there were differences from demand forecasting that did not consider the development plans. Realistic and accurate demand forecasting supported an optimized decision making that efficiently assigns maintenance costs, and can be used as very important basic information for maintenance decision making. CONCLUSIONS : Therefore, decision making for a more efficient and sophisticated road management than the method assuming future traffic can be expected to be the same as the existing pattern or steady traffic demand. The reflection of a reliable forecasting of the future traffic demand to life cycle cost analysis (LCCA) can be a very vital factor because many studies are generally performed without considering the future traffic demand or with an analysis through setting a scenario upon LCCA within a pavement management system.