2013 Student Competition

Transcription

1 ITS Heartland Chapter 2013 Student Competition Shu Yang Saber Abdoli Tiffany M. Rando Smart Transportation Lab Department of Civil Engineering Parks College of Engineering, Aviation and Technology Saint Louis University

2 Potential use of visualizing incident event; Case Study I Abstract Traffic congestion is a growing problem in many metropolitan areas, including our Nation s Capital Region. The number of hours Americans waste sitting in traffic along the Washington DC Metropolitan area ranks either first or second among U.S cities, according to the 2011 Urban Mobility Report. Congestion increases travel time, air pollution, and carbon dioxide (CO2) emissions, and prevents a sustainable economic development. Incidents happen along with congestions. In this paper, different visualization techniques for demonstrating incident trends along our case study (I-495) will be discussed. These visualization methods will help road managers understand incident trends; as a result they can make more effective and efficient decisions to improve traffic condition. Keywords: visualization, incident data, traffic congestion 2. Problem Statement Understanding the large amount of incident data collected from transportation infrastructure is a time consuming and tedious process. Accordingly, spatiotemporal analysis is usually conducted by Geographic Information System experts to convert the raw data to meaningful information. Recent developments in data visualization can help the traffic engineers understand road incident trends more easily and quickly. In this paper, some visualization methods which are interesting for demonstrating particular incident data will be discussed. It is clear that analyzing incident frequency, type, and their spatial and non-spatial attributes will assist the traffic engineers in providing useful and applicable counter measurements, hence decreasing the number of incidents as well as properly managing them and lessening congested traffic.

3 3. Solutions 3.1 Radar chart for demonstrating time-related event The Radar chart is a very effective tool for comparing multiple entities based on different characteristics. A radar chart, also known as a spider chart or a star chart because of its appearance, plots the values of each category along a separate axis that starts in the center of the chart and ends on the outer ring. Temporal events can be demonstrated through a spider chart very effectively; it can serve as a clock panel and help traffic engineers find incident trends during daytime and nighttime very quickly (Figure 1). The number of incidents AM PM Figure 1. Number of incidents based on time As shown in Figure 1, traffic engineers can find the number of incidents that have occurred within a specific time period, and compare the day and night incident trends. It is very straightforward to identify in which time period the incidents happened more frequently. Congestions commonly occur from 7 am to 9 am in the morning, and 4 pm to 7 pm in the afternoon. Based on the number of incidents shown in Figure 1, the radar graph can be utilized to investigate the relationship between the level of congestion and the incidents information. For instance, some indexes that are used for the evaluation of the level of congestion, such as travel

4 time index, planning time index, and buffer time index, could be generated by hours and plotted in Figure 1. This comparison will clearly show the relationship between the level of congestion and the number of incidents. 3.2 Keyword Density Keyword density is the frequency of a keyword or phrase occurrence compared to other keywords or phrases among a data source. This term originally was used by search engines and is an important factor to help search engines rank a webpage. In traffic engineering, it can be used visually to represent the most frequent data relative to the keyword font size. Accordingly, traffic engineers can understand the major issues along the study area very quickly (Figure 2). Figure 2. Visualizing the key contributor of congestion along study area by using keyword density

5 This visualization tool can easily identify the major contributing factor, such as incident type, detection source and weather information, when the traffic becomes congested. As shown in Figure 2, traffic engineers can find the major contributing factor when congestion occurs during the last incident report, and compare the other contributing factors just by looking at each contributing factor s font size and text color. In this case, the type of disabled vehicle happened more frequently than others. 3.3 Commonly used graph Line Graph for demonstrating the trend of events Line graphs provide an excellent way to map independent and dependent variables that are both quantitative. When both variables are quantitative, the line segment that connects two points on the graph expresses a slope, which can be interpreted visually relative to the slope of other lines or expressed as a precise mathematical formula. Figure 3. Visualizing the number of incidents along study area by using line graph Pie Graph for demonstrating the classification of events A pie chart is a circular chart divided into sectors that represent proportions. In Figure 3, the number of incidents during a particular time frame has been illustrated. It can be seen that the

6 highest incident frequency is 29 and occurred on June 29. In Figure 4, the number of incidents is represented in accordance with the source of data, and it can be seen that the CHART unit, MCTMC, and state police are three main information providers in this report. The Number of Incidents Reported by Different Source CCTV CHART Unit Citizen Local Police MCTMC Media Figure 4. Visualizing the number of incidents along study area reported by different source 3.4 Virtual reality through the Google Earth available services Virtual reality is in widespread use in different fields. Virtual reality can make up a virtual environment to model the real world. Google Earth is free software for demonstrating virtual globe, map and geographical information. It integrates terrain, sunlight condition, geometric of roadways, 3D buildings and so on. By using these functionalities, the user will be able to analyze the incident in a virtual presentation of the real conditions, including weather, sunlight and roadway condition, instead of looking into the data through a lot of text. To facilitate the using of incident data, a software program is developed to convert raw incident data in an Excel file to Keyhole Markup Language (KML) which is compatible with Google Earth. KML is an Extensible Markup Language (XML) notation for expressing geographic annotation and visualization within Internet-based, two-dimensional maps and three-dimensional Earth browsers; it is an open data format, which can be used for data exchange and sharing. The feature of KML

7 format can be taken advantage of by traffic engineers to share and collaborate. Figure 5 shows the conversion procedure through various formats of spatial and attribute data to a KML file. Figure 5. Converting spatial and attribute data from various formats to a KML format After transferring the incident data into Google Earth, some additional options will be available for the traffic engineers, such as incident frequency along the road, exploring its attribute data, scrutinizing the location of incidents through the Google Street view, and rebuilding the incident condition by using sunlight function, etc. (Figures 6 to 9). Figure 6. Visualizing the incidents the road on the Google Earth

8 Figure 7. Exploring the incidents attributes through a KML file Figure 8. Using the built-in street view function to show the incident location

9 Figure 9. Utilizing the Google Earth built-in functions to rebuilt the incident condition (This incident happened at 5:43am) Virtual reality through Google Earth allows the traffic engineers to investigate the potential reasons of congestion and incidents through the re-modeled environment, especially with the support of demonstration of the roadway geometry and adjunct facilities, such as traffic signal boards and trees. 4. Conclusion Traffic incidents on metropolitan freeways are causing congestion and delays. The countermeasure development process requires a thorough understanding of the spatial and temporal distribution of incidents. It is known that road incidents commonly form clusters in the geographic space, and over time their occurrences are tied to traffic volumes. The analysis results of the spatial and temporal distributions of road incidents should be presented in an appropriate method to assist traffic engineers in understanding incident patterns. In this paper, we used various techniques to visualize the incident trends in our case study area. First, radar graph was utilized to demonstrate the temporal trend of incidents during daytime and nighttime. Next, density keyword was introduced to effectively represent the major contributors of congestion, and a traditional line graph and pie chart were employed to represent the frequency among the

10 incident data. Finally, Google Earth functionalities were discussed to assist the engineer to scrutinize the spatiotemporal characteristics of each incident. References 1. Black, WR (1991), 'Highway accidents: A spatial and temporal analysis', Transportation Research Record, (1318). 2. Blazquez, Carola A. and Celis, Marcela S. (2012), 'A spatial and temporal analysis of child pedestrian crashes in Santiago, Chile', Accident Analysis & Prevention, 50, Brose, C.A. (1999), 'Geographic Information Systems for Spatial Analysis of Traffic Collision Locations in La Crosse, Wisconsin'. 4. Coate, Douglas and Markowitz, Sara (2004), 'The effects of daylight and daylight saving time on US pedestrian fatalities and motor vehicle occupant fatalities', Accident Analysis & Prevention, 36 (3), Eckley, David C. and Curtin, Kevin M. (2012), 'Evaluating the spatiotemporal clustering of traffic incidents', Computers, Environment and Urban Systems, 37, Erdogan, Saffet, et al. (2008), 'Geographical information systems aided traffic accident analysis system case study: city of Afyonkarahisar', Accident Analysis & Prevention, 40 (1), Wang, Chao, Quddus, Mohammed A., and Ison, Stephen G. (2009), 'Impact of traffic congestion on road accidents: A spatial analysis of the M25 motorway in England', Accident Analysis & Prevention, 41 (4),