Institute of Transportation Studies at UC Berkeley

The use of airport gate electrification infrastructure in the form of ground power (GP) and preconditioned air (PCA) systems can reduce energy and maintenance costs, emissions, and health risks by limiting the use of aircraft auxiliary power unit (APU) engines at the gate. However, their benefits can only be gained when they are actually being used; otherwise, pilots keep APUs on to fulfill their aircraft’s demands for electrical power and air conditioning. GP and PCA systems require a large initial infrastructure investment to increase energy efficiency, and they are installed with the assumption that they will be highly utilized. In this report, a method is developed to examine how much and why GP and PCA are not used to their full potential when they are readily available.

This Preliminary Investigation document, on one hand, outlines challenges encountered by SB/DBEs in the process of getting certified, entering into a contract, executing on projects or delivering services, and sustaining or growing their business. On the other hand, it summarizes OCR’s current ongoing efforts that are aiming to expand contracting with SB/DBEs. Along the way it identifies opportunities that warrant more in-depth investigation for OCR to target its programming and resource allocation as it aims to reduce obstacles or otherwise improve the ability of SBs/DBEs to successfully contract with Caltrans.

The objectives of this study are to formulate policies and practices that can be used to identify place names that have derogatory or racist linkages and provide recommendations on how to rename or remove harmful names and monuments in the California transportation right of way (ROW). This study was requested by the California Department of Transportation and conducted through the University of California, Berkeley Institute of Transportation Studies Technology Transfer Program.

Automated vehicle (AV) deployment can bring about transformational changes to transportation and society as a whole. The infrastructure owner-operators (IOOs), who own, maintain, and operate the infrastructure, have the opportunity to work jointly with the AV industry to provide safe and efficient operations. A key question for the IOOs is, “What transportation infrastructure improvements do AV manufacturers believe will facilitate and improve AV performance?” This study was designed to address this question through a comprehensive survey approach, including an online survey and follow-up interviews. A list of ten questions was discussed, covering the physical and digital infrastructure, infrastructure maintenance, standards and specifications, policy support, data sharing, and so forth. The researchers reached out to more than 60 entities who hold the AV testing permit in California. In total, 20 companies responded. They were from different sectors and well represented the AV industry. From the results of this study, it is concluded that the most important roadway characteristics that have the potential to benefit the automated driving system (ADS) are: (1) digital mapping and signage; (2) lane markings; (3) work zone and incident information; (4) vehicle-to-everything (V2X) communications; (5) actual traffic signals; (6) general signage; and (7) lighting. The digital features considered most critical to help accelerate ADS deployment include work zone and road closure information, traffic signal phase and timing, and traffic congestion. This study provides diverse voices and in-depth insights into topics that the AV industry and IOOs should engage in to advance AVs’ deployment.

Advances in the sharing economy – such as transportation network companies (e.g., Lyft, Uber) and home sharing (e.g., Airbnb) – have coincided with the increasing need for evacuation resources. While peer-to-peer sharing under normal circumstances often suffers from trust barriers, disaster literature indicates that trust and compassion often increase following disasters, improving recovery efforts. We hypothesize that trust and compassion could trigger willingness to share transportation and sheltering resources during an evacuation.

To test this hypothesis, we distributed a survey to individuals impacted by the 2017 Southern California Wildfires (n=226) and the 2018 Carr Wildfire (n=284). We estimate binary logit choice models, finding that high trust in neighbors and strangers and high compassion levels significantly increase willingness to share across four sharing scenarios. Assuming a high trust/compassion population versus a low trust/compassion population results in a change of likelihood to share between 30% and 55%, depending on scenario. Variables related to departure timing and routing – which capture evacuation urgency – increase transportation sharing willingness. Volunteers in past disasters and members of community organizations are usually more likely to share, while families and previous evacuees are typically less likely. Significance of other demographic variables is highly dependent on the scenario. Spare seatbelts and bed capacity, while increasing willingness, are largely insignificant. These results suggest that future sharing economy strategies should cultivate trust and compassion before disasters via preparedness within neighborhoods, community-based organizations, and volunteer networks, during disasters through communication from officials, and after disasters using resilience-oriented and community-building information campaigns.

In the context of implementation of SB 743 (Steinberg, 2013), staff at the California Department of Transportation (Caltrans) have been developing guidance documents on how to calculate induced travel, working with their counterparts at the California Air Resources Board (CARB) and the Governor’s Office of Planning and Research (OPR). OPR’s technical advisory discusses two methods for estimating induced travel: an approach based on the application of travel models and an approach using elasticities drawn from the peer-reviewed literature (such as the National Center for Sustainable Transportation (NCST) induced travel calculator. Caltrans is developing internal guidance to help its analysts choose the best method (or combination of methods) for assessing induced travel from projects on the State Highway System, and has been holding meetings to provide stakeholders with opportunities to express their views and voice their concerns about the drafts

Regret is often experienced for difficult, important, and accountable choices. Consequently, we hypothesize that random regret minimization (RRM) may better describe evacuation behavior than traditional random utility maximization (RUM). However, in many travel related contexts, such as evacuation departure timing, specifying choice sets can be challenging due to unknown attribute levels and near-endless alternatives, for example. This has implications especially for estimating RRM models, which calculates attribute-level regret via pairwise comparison of attributes across all alternatives in the set. While stated preference (SP) surveys solve such choice set problems, revealed preference (RP) surveys collect actual behavior and incorporate situational and personal constraints, which impact rare choice contexts (e.g., evacuations). Consequently, we designed an RP survey for RRM (and RUM) in an evacuation context, which we distributed from March to July 2018 to individuals impacted by the 2017 December Southern California Wildfires (n=226). While we hypothesized that RRM would outperform RUM for evacuation choices, this hypothesis was not supported by our data. We explain how this is partly the result of insufficient attribute-level variation across alternatives, which leads to difficulties in distinguishing non-linear regret from linear utility. We found weak regret aversion for some attributes, and we identified weak class-specific regret for route and mode choice through a mixed-decision rule latent class choice model, suggesting that RRM for evacuations may yet prove fruitful. We derive methodological implications beyond the present context toward other RP studies involving challenging choice sets and/or limited attribute variability.