research report

PRSM Review Year 1 Report A: Review of PRSM Use at Caltrans

Publication Date

September 1, 2017

Author(s)

Iris Tommelein, Nigel Blampied

Abstract

The California Budget Act of 2016 included a provision to “complete a post-implementation review of the Project Resourcing and Schedule Management (PRSM) information technology system upgrade completed by the Department of Transportation.” The PRSM system referenced is Commercial-Off-The-Shelf (COTS) software deployed at Caltrans in 2014 and intended to enable Caltrans to effectively plan State employee and consultant time spent on activities related to projects in its Capital Outlay Support (COS) program. In this Part A report, the researchers studied PRSM as implemented and in practical use, based on a Caltrans document review and interviews conducted with sample groups of Caltrans staff. The result is this factual, non-judgmental description of how PRSM is used and what it is used for. The researchers’ initial sense from the PRSM review meetings is that over the course of three years of deployment, PRSM has become a well-established project management system for approximately 3,000 Caltrans users with read/write access and many others with read-only access, yet PRSM is not yet fully living up to its title. While PRSM is an acronym for “Project Resourcing and Schedule Management,” Caltrans is only using it for project resourcing, especially for annual budgeting, and is not using PRSM’s scheduling functions to their potential.

research report

New Methods for Monitoring Spatial Truck Travel Patterns in California Using Existing Detector Infrastructure

Publication Date

August 1, 2017

Author(s)

Abstract

This study developed a methodology to accurately estimate network-wide truck flows by leveraging existing point detection infrastructure, namely inductive loop detectors. The tracking model identifies individual trucks at detector locations using advanced inductive signatures and matches vehicle pairs at detector locations, using an extended form of the Bayesian classification model to estimate matching and non-matching probabilities of the vehicle pairs Several vehicle feature selection and weighting methods including Self Organizing Map and K-means clustering were applied to better identify individual vehicles from signature data. It was shown that the proposed extensive feature processing enhanced vehicle identification performance even among vehicle pools sharing similar physical configurations. The developed model was tested along an approximately 5.5-mile freeway segment on I-5 and CA-78 in San Diego, California where only 67 percent of the total trucks were observed at both up- and down-stream detector sites. Results showed balanced performances in exactness and completeness of matching with 91 percent of correct outcomes for multi-unit trucks

research report

New Methods for Monitoring Spatial Truck Travel Patterns in California Using Exisiting Dectector Infrastructure

Abstract

This study developed a methodology to accurately estimate network-wide truck flows by leveraging existing point detection infrastructure, namely inductive loop detectors. The tracking model identifies individual trucks at detector locations using advanced inductive signatures and matches vehicle pairs at detector locations, using an extended form of the Bayesian classification model to estimate matching and non-matching probabilities of the vehicle pairs Several vehicle feature selection and weighting methods including Self Organizing Map and K-means clustering were applied to better identify individual vehicles from signature data. It was shown that the proposed extensive feature processing enhanced vehicle identification performance even among vehicle pools sharing similar physical configurations. The developed model was tested along an approximately 5.5-mile freeway segment on I-5 and CA-78 in San Diego, California where only 67 percent of the total trucks were observed at both up- and down-stream detector sites. Results showed balanced performances in exactness and completeness of matching with 91 percent of correct outcomes for multi-unit trucks

research report

Connected and Automated Vehicle Policy Development for California

Abstract

Connected Vehicles (CV), Automated Vehicles (AV), and their combination as Connected Automated Vehicles (CAVs) have been among the most important developments in surface transportation within the past few years. California has been a national leader in the development of these technologies and their predecessors for several decades, but that leadership position is in jeopardy as other states court CAV development and testing outside of California. The paper suggests California actively engages in CAV through a number of different outlets; encouraging the development of state-of-the-art testing facilities where a wider range of vehicles can be tested, building on existing DMV regulatory frameworks, and convening open public discussions about the safety of CAV systems. Public sector engagement and action on this topic are needed in order for California to capitalize on the potential safety, efficiency, and productivity benefits of connected and automated vehicles.

research report

Barriers to Low-Income Electric Vehicle Adoption in California: An Assessment of Price Discrimination and Vehicle Availability

Abstract

Adoption of alternative fuel vehicles by African-American, Hispanic, and low-income consumers has lagged in adoption by Asian, White, and high-income consumers. Understanding the low rate of adoption for certain demographic groups is of particular interest to California. In 2015, the Clean Energy and Pollution Reduction Act (SB 350) was signed into law and requires the California Air Resources Board (CARB) to study barriers to zero-emission transportation options faced by low-income consumers. This study analyzes data for over 400,000 California vehicle sales between 2011 and 2015, containing information on the price paid by the consumer, the location of the dealership, the zip code of the buyer, and buyer demographic characteristics (e.g., race, gender, income, age) for each transaction. Researchers test for the presence of two commonly asserted barriers to electric vehicle (EV) adoption: (1) price discrimination against low-income consumers and (2) limited selection of EVs at dealerships proximate to disadvantaged communities, by comparing the prices and distance traveled for buyers of EVs in different demographic groups.  As a control, researchers compare EV sales to sales of internal combustion engine (ICE) cars. Researchers find little evidence that price discrimination amongst demographic groups or differences in EV availability explains low rates of EV adoption.

policy brief

Using Zero-Emission Vehicles and Other Strategies to Improve Last Mile Deliveries

Abstract

The urban freight system (UFS) is an essential component of the greater freight system and is vital to the urban economy. While the UFS represents a small share of urban traffic, it generates a disproportionate amount of pollution and greenhouse gas emissions, and also has impacts on congestion, safety, and public health. The UFS is largely represented by last mile deliveries, which are characterized as trips that deliver products consumed, or used for other purposes. Last mile deliveries are part of the traditional business to business (B2B) commerce, and the rapidly increasing business to consumer (B2C) and consumer to consumer (C2C) commerce. The UFS is complex and becoming increasingly so as on-demand delivery services proliferate. Online retail sales (B2C) accounted for $394.9 billion or 8.1% of total retail sales in 2016, an increase of 15.1% from 2015i with residential deliveries serving as the main drop-off point for customersii. This trend exacerbates existing challenges for last mile deliveries (e.g., competition for parking, contending with truck size limits and truck technology requirements), and is also requiring a new configuration of the freight system as a whole, and last mile logistics in particulariii . If left unattended, the issues are expected to intensify.

research report

Air Quality and Greenhouse Gas Benefits of an Advanced Low-NOx Compressed Natural Gas Engine in Medium- and Heavy-Duty Vehicles in California

Abstract

The goal of this research is to assess the greenhouse gas (GHG) emissions and air quality (AQ) impacts of transitions to advanced low‐NOx Compressed Natural Gas (CNG) engines in medium-duty vehicle (MDV) and heavy-duty vehicle (HDV) applications in California with a particular emphasis on renewable natural gas (RNG) as a fueling pathway. To evaluate regional air quality impacts in 2035, pollutant emissions from all end-use sectors are projected from current levels and spatially and temporally resolved. Scenarios are constructed beginning with both a conservative (Base Case) and more optimistic (SIP) case regarding advanced vehicle technology and fuel integration to provide a spanning of potential impacts. To capture the impact of seasonal dynamics on pollutant formation and fate, two modeling periods are conducted including a winter and summer episode. To estimate the potential GHG impacts of transitions to advanced CNG engines in HDV and MDV, scenarios are evaluated under various assumptions regarding fuel pathways to meet CNG demand from a life cycle perspective. Scenarios are compared to the baseline cases assuming (1) all CNG is provided from conventional fossil natural gas and (2) under a range of possible resource availabilities associated with renewable natural gas and renewable synthetic natural gas (RSNG) from in-state resources. Key findings include: i) expanding the deployment of advanced CNG MDV and HDV can reduce summer ground-level ozone concentrations and ground-level PM2.5 in key regions of California; ii) the largest AQ benefits are associated with reducing emissions from HDV; iii) in-state renewable natural gas pathways can meet the CNG demand estimated for both baseline cases; iv) in-state resources are unable to entirely meet CNG demand for the high total CNG demand estimated for the majority of Base alternative cases, and v) advanced CNG HDV and MDV can moderately reduce GHG emissions if fossil natural gas is used (14 to 26%).

research report

mAPPing Roadkill to Improve Driver and Wildlife Safety on Highways

Abstract

This application framework will provide Caltrans staff and partners the ability to easily collect information on roadside features and document their findings in a web-based database that can be shared within Caltrans, as well as externally. The “one-click” application allows users to simply point at a roadside feature, and snap a photo, and this information will automatically be sent to the image server for processing. Additional annotations can be added (such as an animal species), which will provide the necessary verification steps for a complete and permanent record. These data are a critical first step to mitigating impacts to drivers and animals from collisions.

research report

California Feebate: Revenue Neutral Approach to Support Transition Towards More Energy Efficient Vehicles

Abstract

Markets and regulations are getting out of alignment due to vehicle fuel economy and greenhouse gas standards becoming increasingly stringent. If gasoline prices stay relatively low, then consumers will have little incentive to purchase more expensive fuel-efficient vehicles. California can provide tax incentives to consumers to purchase more fuel-efficient vehicles, but the cost to taxpayers of doing so grows exponentially if sales of these vehicles increase. As a result, this report explores the possibility of imposing fees for less fuel-efficient vehicles and smaller rebates for more fuel-efficient cars and trucks. The goal of the proposed program is to design a revenue-neutral program that corrects market signs to consumers and provides an incentive to purchase higher fuel-efficient vehicles.  

research report

Electric Utility Rate Design and Transportation Electrification

Abstract

This report outlines the development of an electric utility billing system model for the purpose of evaluating existing and potential electric utility rate schedules. The model was primarily developed to evaluate the cost implications of existing and proposed rate schedules on customers charging electric vehicles (EV) but can also be used to evaluate residential electric power bills across a broader context of economic and policy issues.  The first issue analyzed in this report is the differential impact of residential default inclining block (tiered) and optional time-of-use rates on the average customer’s cost of electric vehicle charging. The analysis shows that the average customer’s cost of electric vehicle charging is minimized by the adoption of time-of-use rates. The second issue analyzed in this report is the impact of current demand charges on the bills of commercial customers using direct current fast charging for electric vehicles.