Recent technology trends have allowed affordable and efficient collection of driver data. This has enabled a variety of potential applications, including more accurate pricing determinations for insurance and finer grained traffic planning for improved public safety. Although this technological growth provides for a wealth of new opportunities, given the safety implications of driving, there are many security and privacy issues that must be considered for their deployment.

The goal of this project is to develop a comprehensive framework with a set of models to improve multi-modal traffic signal control, by incorporating advanced floating sensor data (e.g. GPS data, etc.) and traditional fixed sensor data (e.g. loop detectors, etc.). In order to accomplish this goal, we completed five tasks. First, we conduct a comprehensive survey of transportation professionals, who can bring up existing state-of-practice, open issues and future challenges in multi-modal traffic signal control.

Unmanned aircraft systems (UAS) and unmanned ground systems (UGS) have the potential to change the way we perform some of transportation-related operations. Nowadays, opportunity arises to leverage various innovative technological capabilities to explore their use and value in real wor1d operating environments. Specifically, exploring the capabilities of UAS, also called drones, and UGS in specific transportation areas appears to have significant potential.

Permeable pavements such as porous asphalt, pervious concrete and permeable interlocking concrete pavers are relatively novel alternatives to conventional pavement that allow rain and snowmelt to infiltrate, thereby reducing runoff, flooding and nonpoint source pollution. A barrier to wider adoption of these runoff-reducing alternative pavements is uncertainty over their long-term performance. Infiltration capacity (IC) can decrease over time if pores in permeable pavement become clogged with particles.

Currently, UPRM is working on a mobile computing application for automating the collection process of field inspection data using iPads or Android Tablets. The application contains standard forms of the specifications that appear in the Standard Specification of Road and Bridge Construction book. Because of their high mobility characteristics and due to their small size and light weight, these mobile devices can be used in the construction field to perform various tasks including development and evaluation of a change order and extra work.

NJ TRANSIT customers can use a smartphone application (“app”) to purchase tickets and access transit information. Most smartphones are equipped with technology that can determine the user’s location; however, this feature is currently used in a limited capacity in NJ TRANSIT’s app. By knowing a customer’s location, NJ TRANSIT could potentially provide customized information directly to passengers based on their location, which is referred to as geotargeting.

This report addresses the capacitated vehicle routing problem (CVRP) and the split delivery vehicle routing problem (SDVRP) with uncertain travel times and demands when planning vehicle routes for delivering critical supplies to the affected population in need after a disaster.

This research work examines the relationships among: (i) Sense of Place (SOP); (ii) non-motorized sustainable travel choices and accessibility; and (iii) adoption and use of information and communication technologies (ICT). A guiding principle in designing the built environment for sustainability and livability is the latent construct of Sense of Place (SOP) which leads visitors to perceive and associate a strong identity or character with a particular location.

New technologies such as global positioning system, smartphone, and social media are changing the way we move around. Traditional transportation research has overwhelmingly emphasized the collection of quantitative data for modeling, without much collection of qualitative data to understand the processes of why and how individuals make their travel choices. We developed a prototype in this project to use realtime GIS and social media (Twitter) to collect, analyze, and display qualitative travel information from individuals. There are two goals in this research project.

The goal of this project is to develop a comprehensive framework with a set of models to improve multi-modal traffic signal control, by incorporating advanced floating sensor data (e.g. GPS data, etc.) and traditional fixed sensor data (e.g. loop detectors, etc.). In order to accomplish this goal, we completed five tasks. First, we conduct a comprehensive survey with transportation professionals, who can bring up existing state-of-practice, open issues and future challenges in multi-modal traffic signal control.