Multi-Modal Transportation Systems

description

Massive data, from various sources, provides unprecedented opportunity for the transportation industry to understand travel behavior and to propose efficient management strategies. However, those data sources are usually established by disparate public agencies and private sectors. They rarely communicate with each other and, as a result, data is only used and analyzed for a particular piece of a transportation system, such as an intersection, a stretch of freeway, or bus routes operated by the same agency. With disparate data sources, each part of the system is individually operated, making the entire transportation system far from socially optimal.

MAC has been collecting and integrating various multi-modal data sets around the Pittsburgh region, which include roadway traffic, probe vehicles, public transit, parking, incidents, bicycles, buildings, energy consumption, emissions, social media, etc. The data infrastructure is established on hierarchical systems, large-scale data archives and integration, statistical learning algorithms, and optimal decision making processes. Multi-modal transportation systems are analyzed integratively. The decision-making of one mode of transportation system must take into account its impact to other modes and vice versa. The data infrastructure is closely aligned to on-going research at Carnegie Mellon, including urban systems, air quality studies, climate change, connected/autonomous vehicles, energy policies, and infrastructure life cycle analysis. Interactions with those groups in other disciplines will have synergistic effects on the integration and optimization of sustainable urban systems.

Featured projects

Mobility Data Analytics Center: the Engine for Building a Regional Traveler Information System for Pittsburgh

Team: Sean Qian (PI, CMU), Xidong Pi (CMU), Zhangning Hu (CMU)
Funding source: Benedum Foundation
Start/End time: 2013-2014

Landing page of Mobility Data Analytics WebUI.

MAC is developing a centralized data engine supported by a web application to manage and analyze massive data. The data engine essentially sets protocols for data exchange from various sources, and is necessary to accommodate the needs of data fusion and analytics. The engine offers organization, visualization and analytics of a wide array of mobility data, roadway, incidents, parking, public transit, weather, electric vehicles, mobile, etc. Furthermore, the engine can translate the data into useful information for people who need it: legislators, transportation planners, engineers, researchers, travelers, and companies. Unlike the traditional single computer stand-alone software or tools for data preparation and decision making, the data engine is accessed by users through web-based data sharing and browser-based human-computer interaction. The web application visualizing data and recommending decisions serves the front end of the data engine.

Publication

Sean Qian, Xidong Pi, Zhangning Hu (2014), “Mobility Data Analytics Center”, Technology for Safe and Efficient Transportation. [URL]

Building an Accessible, Low-Stress, Safe, and Sustainable, Bicycle Infrastructure Network for City of Pittsburgh

Team: Sean Qian (PI, CMU)
Funding source: U.S. Department of Transportation through National University Transportation Center (T-SET: Technologies for Safe and Efficient Transportation)
Start/End Time: 2017-2018

Transit Service Performance Analysis and Bunching Detection Using Automatic Passenger Counters (APC) and Automatic Vehicle Location (AVL) Data

Team: Sean Qian (PI, CMU), Xidong Pi (CMU)
Funding source: Pennsylvania Infrastructure Technology Alliance
Start/End Time: 2014-2015

The essential idea is to fully utilize the big data in public transit to provide travelers fine-grained customizable information regarding transit service performance (efficiency, reliability and quality). By monitoring day-to-day transit service and how users respond to information provision, we can develop a better understanding of travelers’ preferences on efficiency, reliability and quality of transit service, as well as their modal choices. Big data and data-driven behavioral models facilitate agencies’ decision making (such as scheduling). Effective information provision, along with data-driven scheduling, holds great potential to improve the service performance and travelers’ riding experience.

Publication

Xidong Pi, Sean Qian, Jackson Whitmore, Amy Silbermann (2017), “Understanding Transit System Performance Using APC/AVL Data: a Transit Data Analytics Platform With a Case Study for the Pittsburgh Region”, Journal of Public Transportation, accepted and forthcoming