Doctoral Dissertation: Big Data for Urban Sustainability: Integrating Travel Patterns in Environmental Assessments

Event Type: 
Hua Cai, Ph.D. Candidate
Friday, June 26, 2015 - 9:00am to 10:00am
2024 Dana Building
Event Sponsor: 
School of Natural Resources and Environment

Date: June 26, 2015
Time: 9:00 AM
Location: 2024 DANA Building

Title: "Big Data for Urban Sustainability: Integrating Travel Patterns in Environmental Assessments”

​Dissertation Committee:
Ming Xu, Chair (SNRE/CEE)
Peter Adriaens, Co-Chair (CEE/Ross)
Jerome Lynch (CEE)
Gregory Keoleian (SNRE/CEE)
Carl Simon (FSPP)
Ji Zhu (STATS)

To alleviate fossil fuel use, reduce air emissions, and mitigate climate change, “new mobility” systems start to emerge with technologies such as electric vehicles, multi-modal transportation enabled by information and communications technology, and car/ride sharing. Current literature on environmental implications of these emerging systems is often constrained by using aggregated travel pattern data to characterize human mobility dynamics, neglecting the individual heterogeneity. Individual travel patterns affect charging behavior, connection needs between different transportation modes, and car/ride sharing potentials for these emerging systems, which are key factors determining the potential environmental impacts. Therefore, to better understand these emerging systems and inform decision making, travel patterns at the individual level need to be taken into account in environmental assessments. Using vehicle trajectory data of over 10,000 taxis in Beijing, this research demonstrates the benefit of integrating individual travel patterns in environmental assessments through three case studies (vehicle electrification, charging infrastructure siting, and ride sharing) focusing on two emerging systems: electric vehicles and ride sharing. Results from the vehicle electrification case study show that individual travel patterns can impact the environmental performance of fleet electrification. When unit battery cost is above $200/kWh, vehicles with greater battery range cannot result in higher level of travel electrification. The public charging station siting case study demonstrates that individual travel patterns can better estimate charging demand and guide public charging infrastructure development. Charging stations sited according to individual travel patterns can electrify more vehicle-miles-traveled. Lastly, results from the ride sharing case study indicate that trip details extracted from vehicle trajectory data enable dynamic ride sharing modeling. Shared taxi rides can significantly reduce total travel distance and air emissions. Only minimal amount of tolerance to travel time change is required from the riders to enable ride sharing. In summary, vehicle trajectory data can be integrated into environmental assessments to capture individual travel patterns and improve our understanding of the emerging transportation systems.

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