Branislav Dimitrijevic

Application of TELUM by the Pikes Peak, Colorado, Area Council of Governments: Lessons Learned in Colorado Springs

For U.S. metropolitan planning organizations (MPOs), obtaining, preparing, and validating socioeconomic forecasts are fundamental tasks to ensure that logical, consistent, and approved population and employment data are provided to the travel demand models. This process is enhanced if the MPO includes both transportation and land use inputs into the forecast process. To fulfill these desires, the Pikes Peak Area Council of Governments (the MPO in Colorado Springs, Colorado) decided to use TELUM–-transportation, economic, and land use model software–-to conduct the land use forecasting task. TELUM was developed by Stephen Putman and the New Jersey Institute of Technology (NJIT) as part of the transportation, economic, and land use system sponsored by FHWA. With assistance from NJIT, MPO staff serving this 500,000-plus person region calibrated the land use model for the 2000 to 2005 period and then used TELUM to produce land use forecasts for six 5-year increments between 2005 and 2035. The Colorado Springs region has a unique character, including a workforce that is 11% active military personnel, a diverse employment profile including high-tech manufacturing and a large recreation-based workforce, and a growing retiree population. Additionally, the MPO area includes a dense urban area, large exurban developments, rugged mountain areas, and frontier grasslands, all of which challenged the technical staff that worked on model development. The technical and policy insights and lessons learned from the TELUM application in Colorado Springs have transferable value for all sizes of MPOs faced with forecasting development and transportation.

Smart Arrival Notification System for Americans with Disabilities Act Passenger Paratransit Service with a Consumer Mobile Device

This research presents an arrival notification system for paratransit passengers with disabilities. Almost all curb-to-curb paratransit services have a significantly large pickup time window, ranging from 20 to 40 min from the scheduled time and producing substantial passenger waiting times. The arrival notification system presented in this study delivers an automated voice call to a registered user once the paratransit vehicle is in proximity to the pickup location. The system utilizes the Google Traffic application programming interface (API) for the vehicle arrival estimation. Unlike other vehicle arrival notification systems in the state of the practice, the proposed system is compact and does not require additional equipment such as radio transmitting and positioning devices. The proposed system, which uses consumer mobile devices with the Android or iOS platform, is designed to exploit commercial cellular network service (i.e., 3G and 4G-LTE). In addition to the passenger notification, the proposed system provides paratransit drivers with real-time route guidance information developed through the Google Maps API. Field evaluation conducted in Essex County, New Jersey, revealed significant reduction in passenger waiting time. The passenger waiting time was reduced by 15 to 20 min. In addition, the accuracy of the notification system was tested. During the test, in almost all cases, the vehicle arrived 1 min earlier than the proposed arrival time.

Performance Evaluation of Video Analytics for Traffic Incident Detection and Vehicle Counts Collection

Current incident detection and traffic monitoring method using closed-circuit television (CCTV) cameras meets with limitations as the coverage of CCTV cameras rapidly expands. In general, traffic operators at Traffic Operation Center (TOC) have to manage and monitor numerous CCTV cameras deployed on roadways. Thus, many transportation agencies consider the use of video analytics system to reduce incident detection time and minimize traffic impacts, but they also want to validate the performance of the video analytics system whether it can work with their existing video surveillance infrastructure before procuring the system. To that end, a pilot study was designed and conducted to evaluate the accuracy of a video analytics product by integrating with CCTV cameras deployed on highways. The pilot study was designed to evaluate the accuracy of video analytics in detecting incidents and collecting traffic counts. The test results show that the performance of video analytics is significantly impacted by video quality and other environmental factors such as lighting and weather conditions.