Efficient Mobility Portfolio Schemes for Integrated, Intermodal, and Incentivized Shared Transportation

Status

In Progress

Project Timeline

January 1, 2018 - December 31, 2018

Principal Investigator

Project Summary

Flexible transportation options such as ridesharing, carsharing, and bikesharing can be effective feeders for public transportation service because these new options have higher accessibility to travelers’ origin and destination. The private sector that now plays an increasing role as a component of urban transportation can augment public transportation accessibility. In other words, Transportation Network Companies (TNCs) and Mobility Service Providers (MSPs) can be complementary modes to transit system by providing shared use of transportation capacity. This option requires a matching algorithm between drivers and riders. Implementing a comprehensive multi-modal multiple-option shared travel system in an urban area is required to improve efficiency of transportation systems. This integrated platform can identify various travel options and recommends travelers with tailored information. Some options might have a route consisting of multiple modes (i.e.: ridesharing- transit-bikesharing). Some options might suggest a detour, compared to the shortest path what user’s primary mode has. To compensate the loss of users’ utility by giving up their primary mode and to encourage use of alternative mode option, the optimal incentive scheme will be proposed. This proposal is to study the benefits of a smart mobility portfolio which is a trip planning system integrating multiple travel modes and including an incentive scheme. A smart mobility portfolio can provide time-dependent trip plans across multiple modes that include several options such as shared-cars, shared-rides, bikesharing, bus/rail transit and combinations of travel modes. Four main components of this system are: 1) to identify travel options for achieving efficiency under current traffic conditions and supply conditions, 2) to calculate proper amounts of incentive to attract people to choose one of the options, 3) to provide travelers with the best travel options among these alternatives, and 4) to examine the impact of the proposed method to entire transportation systems and to update the parameters in the model. Encouraging people to change their current travel behavior through the smart mobility portfolio platform, we expect the improvement of traffic network efficiencies and the contribution to eco-friendly environment by reducing traffic congestion.

Simulating Life with Personally-Owned Autonomous Vehicles through a Naturalistic Experiment with Personal Drivers

Status

Complete

Project Timeline

July 1, 2017 - December 31, 2020

Principal Investigator

Project Summary

To date, little is known about how travel will change with self-driving vehicles. The biggest difference in using a self-driving car, and arguably the feature that will cause the most change in travel behavior, is not having to be behind the wheel driving the car or having to be in the car at all as it travels from one place to another. Existing behavioral studies exploring this unknown future are limited because they either focus on safety and human factors rather than travel behavior, assume travel behavior implications, or ask about hypothetical scenarios that are unfamiliar to the subjects. This project will explore the potential impacts of self-driving vehicle deployment on travel patterns and behavior through a naturalistic experiment. Households in the greater Sacramento area will be recruited to participate in a three-week travel study. All travel during this time will be monitored and tracked. At the beginning of the three-week period, participants will take part in a one-on-one entrance interview to receive an overview of self-driving cars and share their attitudes toward self-driving cars. During the second week, participants will have access to a professional driver for a set block of time. Having access to a driver will relieve participants from the duty of personally driving or physically being in a car when the car is making trips, while maintaining the other aspects of owning a personal vehicle (e.g. fuel and other vehicle costs). Upon completion of the three-period, participants will take part in an exit survey similar to the entrance survey in terms of (re)asking about attitudes toward self-driving cars in addition to being asked about their experience with the simulated self-driving car experience. This experiment leverages a framework that has already been beta tested providing evidence of value.

Collection of Activity Data from On-Road Heavy-Duty Diesel Vehicles

Status

Complete

Project Timeline

Principal Investigator

Kanok Boriboonsomsin

An Analysis of Travel Characteristics of Carless Households in California

Status

Complete

Project Timeline

Principal Investigator

Project Team

Suman Mitra

Campus(es)

UC Irvine

Linking Statewide and Regional Travel Models to Estimate Interregional Travel Impacts in California

Status

In Progress

Project Timeline

October 1, 2016 - June 30, 2024

Principal Investigator

Campus(es)

UC Irvine

Project Summary

Metropolitan Planning Organizations (MPOs) use regional travel forecasting models to estimate vehicle trips (VT), average speeds, and vehicle miles traveled (VMT), which serve in turn as input to regional emission models. Interregional travel is not usually part of MPO models, but it is explicitly part of statewide models. The California Statewide Travel Demand Model (CSTDM) is an activity-based model that produces statewide origin-destination trip tables for assignment to the statewide network. Consistency tests, however, suggest that there are significant deviations between link counts from the CSTDM and those from regional models, as measured at defined cordon stations. These trip counts are, by definition, interregional travel – travel that is typically generated within a region but with performance impacts in another region or in areas not formally part of a defined region.

The proposed project seeks to develop and test methods to synchronize the travel forecasting results of the CSTDM with regional travel forecasting models, with the objective of better estimating interregional travel and greenhouse gas emissions in California. Whether trip-, tour-, or activity-based, CSTDM and all current regional models apply conventional trip assignment as the last step in the modeling process. From the perspective of potential policies to address performance impacts, this study will resolve how regions and the state properly account for the relative proportion of interregional travel and the associated travel impacts. The methodological problem is to synchronize the assigned and validated cordon counts produced by regional models with those generated as part of assignment in the CSTDM. Techniques to modify origin-destination trip tables exist but applications above the local area have been rare. The CSTDM trip tables will be updated to reflect the assigned counts at defined MPO cordon stations. At least two methods will be tested using Caltrans’ Performance Measurement System (PeMS) data with CSTDM trip tables and using MPO cordon estimates with CSTDM trips tables. Each method will be evaluated, with one selected for final application based on its consistency across all model levels and data sources.

Do Compact, Accessible, and Walkable Communities Promote Gender Equality?

Status

Complete

Project Timeline

Principal Investigator

Clean Air in Cities: Impact of the Layout of Buildings in Urban Areas on Pedestrian Exposure to Ultrafine Particles from Traffic

Project Summary

Southern California is no stranger to auto-related pollution. Areas near roadways typically demonstrate much higher pollutant concentrations; as a result, pedestrians and residents in these areas face greater exposure to air pollutants. In dense urban areas like Los Angeles, near-roadway environments can include most street-level outdoor spaces. At the same time, traffic-related pollution levels in urban areas are highly variable. Although the connection between built environment and street-level pollutant concentrations is a nascent field of study, it is clear that the design of the built environment plays a major role on pollution concentration.

The researchers examined the effects of different built environment designs on the concentrations of street-level ultrafine particles (UFP) at the scale of several blocks using the Quick Urban and Industrial Complex (QUIC) numerical modeling system. They evaluated the effects of several built environment designs, changing building heights and spacing while holding total built environment volumes constant. They found that ground-level open space reduces street-level pollutant concentrations. Holding volume/surface area constant, tall buildings clustered together with larger open spaces between buildings resulted in substantially lower pollutant concentrations than buildings in rows. Buildings arranged on a ‘checkerboard’ grid with smaller contiguous open spaces, a configuration with some open space on one of the sides of the roadway at all locations, resulted in the lowest average concentrations for almost all wind directions. Rows usually prohibit mixing for perpendicular and oblique wind directions, even when there are large spaces between them, and clustered buildings have some areas where buildings border both sides of the roadways, inhibiting mixing. The model results suggest that pollutant concentrations drop off rapidly with height in the first 10 m or so above the roadways. In addition, the simulated vertical concentration profiles show a moderate elevated peak at the roof levels of the shorter buildings within the area. Model limitations and suggestions both for urban design are both discussed.