The Institute of Transportation Studies at UC Berkeley has supported transportation research at the University of California since 1948. About 50 faculty members, 50 staff researchers and more than 100 graduate students take part in this multidisciplinary program, which receives roughly $40 million in research funding on average each year. Daniel Rodriguez, Professor of City and Regional Planning, is its director.
These problem sets comprise a supplement to Fundamentals of Transportation and Traffic Operations (C. Daganzo, Pergamon, 1997). Academicians can also obtain a companion set of solutions by writing to "Institute of Transportation Studies, Publications Office, 109 McLaughlin Hall, University of California, Berkeley, CA 94720" or by sending e-mail to firstname.lastname@example.org.
Optimal Infrastructure System Maintenance and Repair Policies with Random Deterioration Model Parameters
Accurate facility deterioration models are important inputs for the selection of Infrastructure Maintenance, Repair, and Reconstruction (MR & R) policies. Deterioration models are developed based on expert judgment or empirical observations. These resources, however, might not be sufficient to accurately represent the performance of infrastructure facilities. Incorrect deterioration models may lead to wrong predictions of infrastructure performance and selection of inappropriate MR & R policies. This results in higher lifecycle costs. Existing infrastructure MR & R decisionmaking models assume that deterioration models represent the real deterioration process of infrastructure facilities. This assumption ignores the uncertainty in empiricallyderived facility deterioration models. This dissertation presents a methodology for selecting MR & R policies for systems of infrastructure facilities under uncertainty in the deterioration model parameters. It is assumed that inspections reveal the true conditions of facilities. Based on the inspection results, the deterioration model parameters can be updated to express the deterioration process more accurately. It is expected that more appropriate maintenance policies will be selected as a result. In the first part of this dissertation, it is assumed that facility inspections are performed at the beginning of every year. The model parameters are updated and MR & R policies are selected every year using the updated deterioration models. In the second part, the assumption is relaxed and alternate inspection frequencies are considered. In this case, the updates of the model parameters and the selection of optimal MR & R policies are executed only after an inspection. The results of the parametric analyses demonstrate that updating the deterioration models reduces the expected system costs. The results also show that relaxing the facility inspection frequency can reduce the total costs further.
This dissertation presents dynamic stochastic optimization models for Air Traffic Flow Management (ATFM) that enables decisions to adapt to new information on evolving capacities of National Airspace System (NAS) resources. Uncertainty is represented by a set of capacity scenarios, each depicting a particular time-varying capacity profile of NAS resources. We use the concept of a scenario tree in which multiple scenarios are possible initially. Scenarios are eliminated as possibilities in a succession of branching points, until the specific scenario that will be realized on a particular day is known. Thus the scenario tree branching provides updated information on evolving scenarios, and allows ATFM decisions to be re-addressed and revised. First, we propose a dynamic stochastic model for a single airport ground holding problem (SAGHP) that can be used for planning Ground Delay Programs (GDPs) when there is uncertainty about future airport arrival capacities. Ground delays of non-departed flights can be revised based on updated information from scenario tree branching. The problem is formulated so that a wide range of objective functions, including non-linear delay cost functions and functions that reflect equity concerns can be optimized. Furthermore, the model improves on existing practice by ensuring efficient use of available capacity without necessarily exempting long-haul flights. Following this, we present a methodology and optimization models that can be used for decentralized decision making by individual airlines in the GDP planning process, using the solutions from the stochastic dynamic SAGHP. Airlines are allowed to perform cancellations, and re-allocate slots to remaining flights by substitutions. We also present an optimization model that can be used by the FAA, after the airlines perform cancellation and substitutions, to re-utilize vacant arrival slots that are created due to cancellations. Finally, we present three stochastic integer programming models for managing inbound air traffic flow of an airport, when there is adverse weather impacting the arrival capacity of the airport along with its arrival fixes. These are the first models, for optimizing ATFM decisions, which address uncertainty of future capacities of multiple NAS resources.
This research describes field studies of how on-ramp metering can increase the capacity of freeway merges. Some effects of on-ramp metering have been known for a long time. We have known that on-ramp metering can 1) increase freeway flow and speed upstream of a merge; and 2) reduce system-wide delay by alleviating gridlock-causing queues that have blocked off-ramps. However, past studies have not conclusively shown that on-ramp metering can increase the maximum outflow (capacity) of freeway merges. The experiments conducted in the present study verify that on-ramp metering can increase freeway merge capacities. Detailed traffic data collected from videos for more than 30 rush periods at two merge bottlenecks unveil six major research findings: 1) merge capacity diminishes after merges became active bottlenecks; 2) the mechanism of "capacity drop" has been identified and was found to be reproducible across all days and it both sites. By metering the on-ramp in certain strategic ways, the capacity drop mechanism can be 3) reversed; and 4) even averted; 5) such metering strategies can be fully automated using loop detector measurements; and 6) control strategies other than ramp metering also hold promise for increasing merge capacities. These findings provide much-needed information concerning how to control freeway traffic. They also offer basis for more realistic theories of merging traffic flow.
Transportation Periodicals And Newsletters Currently Received At The Institute Of Transportation Studies Library, University Of California At Berkeley
This publication is intended to serve as a convenient reference to selected transportation periodicals and newsletters currently (2000) received by UC Berkeley's Harmer E. Davis Transportation Li-brary. This latest version of Transportation Periodicals and Newsletters represents a thourough revision of earlier editions (1989, 1993, and 1995) published under the same (or similar) title. The subject content of this listing reflects the subject strengths of the H.E. Davis Transportation Library: highways and traffic, air transportation, railroads, and urban transit. Water and pipeline modes are represented to a lesser extent. Collection emphasis at the Transportation Library is placed on planning, design, construction, and operations and most titles fall within this context. However, some titles in transportation business and economics do appear. While primary emphasis is placed on English language publications published in the United Sates, significant transportation journals from abroad are also included in the pages which follow.
This bibliography is intended to serve as a guide to the major sources ofinformation in Intelligent Transportation Systems (ITS). While the focus is on the United States, some international materials have been included. Emphasis is on current materials, although publications of historical interest have also been included. Resources listed include print and electronic materials, as well as websites on the Internet. This bibliography is based primarily on the holdings of the Harmer E. DavisTransportation Library at the Institute of Transportation Studies, University of California at Berkeley. References for electronic documents and resources, and websites, are current as of November 2006.The electronic version of this bibliography is a component of the 6th edition of Sources of Information in Transportation, a collaborative effort by members of theTransportation Division of the Special Libraries Association.
This bibliography, containing over 650 entries, is intended to serve as a guide to the major sources of information on highways. While sources listed focus primarily on the United States and Canada, some international materials have been included. Though emphasis is on current publications, some materials of historical interest have also been included. Resources listed in the bibliography include both print and electronic materials, with many Internet sites falling within that latter category. The bibliography was a collaborative effort and was compiled by twelve members of the Transportation Division of the Special Libraries Association. The resultant bibliography is part of a larger Transportation Division project, a revised edition of the Division's multi-volume work, Sources of Information in Transportation. The full, multi-volume new edition (its 5th ) will be available in electronic format on the Internet, with a publication date of late 2001 anticipated. Information concerning the new edition of Sources of Information in Transportation may be found at the Division's website: http://www.library.nwu.edu/transportation/slatran/
A significant portion of the population stayed, and continue to stay, at home due to the COVID-19 pandemic. With more people staying home, online shopping increased along with trips related to pickups and deliveries. To gain a better understanding of the change in retail purchases and related travel, UC Berkeley researchers compared pre-pandemic shopping to pandemic-related shifts in consumer purchases in the greater Sacramento area for nine types of essential and non-essential commodities (e.g., groceries, meals, clothing, paper products, cleaning supplies). In May 2020, the research team resampled 327 respondents that participated in the 2018 Sacramento Area Council of Governments (SACOG) household travel survey. The 2018 SACOG survey collected responses over a rolling six-week period from April to May 2018 and asked residents about their motivations for, attitudes toward, and ease of use of online shopping. They were also were asked about the number of e-commerce purchases made, and the number of deliveries and pickups made from those e-commerce purchases for each commodity type. In addition, respondents also reported changes (less or more) in their behavior from a typical week in January or February 2020 (prior to the COVID-19 pandemic) for: 1) tripmaking, e-commerce purchases, and delivery and pick up frequencies; 2) purchase sizes; 3) distances traveled; and 4) modes used for in-person trips. This brief highlights findings from an analysis on changes in frequency of purchases, deliveries and pickups, and order sizes.
2001: An Airspace Odyssey SUMMARY PROCEEDINGS OF THE 2001 AIRPORT NOISE SYMPOSIUM AND AIRPORT AIR QUALITY SYMPOSIUM
These proceedings summarize the presentations made at the 16th Airport Noise Symposium and 2nd Airport Air Quality Symposium, organized by the Technology Transfer Program of the Institute of Transportation Studies (ITS) and held in San Diego, California, from February 25 to March 2, 2001. The presentation slides for many of the presentations at both symposia are available on the ITS Technology Transfer Program website at .
The symposia were organized in conjunction with the National Center of Excellence for Aviation Operations Research, the Federal Aviation Administration, the Federal Interagency Committee on Aviation Noise, and the Port of San Diego, and with the active support and assistance of the individuals and organizations represented on the Symposia Program Committee, listed at the end of these proceedings.
This paper proves that kinematic wave (KW) problems with concave (or convex) equations of state can be formulated as calculus of variations problems. Every well-posed problem of this type, no matter how complicated, is reduced to the determination of a shortest tree in a relevant region of spacetime where cost is predefined. A duality between KW theory and /least cost networks is thus unveiled. In the new formulation space-time curves that constrain flow, such as sets of moving bottlenecks, become space-time shortcuts. These shortcuts become part of the network and affect the nature of the solution but not the speed with which it can be obtained. Complex boundary conditions are naturally handled in the new formulation as constraints/shortcuts of this type.
This paper shows how to reduce the bullwhip effect by introducing advance demand information (ADI) into the ordering schemes of supply chains. It quantifies the potential costs and benefits of ADI, and demonstrates that they are not evenly distributed across the chain. Therefore, market-based strategies to re-distribute wealth without penalizing any supplier are presented. The paper shows that if a centralized operation can eliminate the bullwhip effect and reduce total cost, then some of this reduction can also be achieved with decentralized negotiation schemes. Their performance is evaluated under different modes of probabilistic supplier behavior. For some forms of behavior the optimum is reached. But if suppliers are greedy and impatient the expected gain in wealth is relatively small. This is a case of economic "market failure."
We consider a Generalized, Multiple Depot Hamiltonian Path Problem (GMDHPP) and show that it has an algorithm with an approximation ratio of 3/2 if the costs are symmetric and satisfy the triangle inequality. This improves on the 2-approximation algorithm already available for the same.
The objective of this report is to first review what we know from the literature about long distance travelers, analyze the contents of the long distance travel log of the California Household Travel Survey (CHTS), demonstrate the augmentation of the trip/tour records with destination attractiveness indicators, derive prototypical traveler profiles, and provide amore detailed analysis of long distance tours. The data are from a simplified travel log that asked respondents from households to report all the trips 50 miles or longer they made in the 8-weeks preceding the day they were assigned a full travel diary. The survey instrument used for this reporting is shown in Figure 1. In this report we identify a few issues with the data collected using this travel log, and these issues motivate us to also investigate the long distance travel reported in the daily diary. The range of variables that we can analyze depends heavily on the accuracy with which respondents reported their trips, and we found they were generally more accurate in the daily diary. However, the long distance travel log contains data that span longer periods than 24 hours and therefore a better record of trips with overnight stays away from home. Past studies of long distance travel have found that commuting by people who sought out lower cost housing is a major contributor to long distance travel, and that higher income and employed persons travel more, but there are multiple shortcomings in the literature that we seek to address here. The literature contains a variety of definitions for “long distance” travel, including ones based on distance (e.g., longer than 50 miles, 100 miles, or longer than 100 kilometers) and travel time (e.g., 40 minutes). Long distance travel researchers have considered a variety of indicators including number of long distance trips, activity before and/or after commute, mode used, time of day of trip, and destination (Georggi and Pendyala, 2000, Axhausen, 2001, Beckman and Goulias, 2008, LaMondia and Bhat, 2011, Caltrans, 2015, Shahrin et al., 2014, Holz-Rau et al., 2014). Most studies did not address trip chaining (e.g., people going to a work place, then to a leisure destination, and then back home). Very little analysis is also found in differentiating trips with an overnight stay, despite the important differences between these trips and daily commuting. The choice of analysis in past studies was presumably driven by: a) an emphasis in the literature on trips to and from work; and b) a focus on a single trip by an individual person as the unit of analysis instead of multiple trips from household members. This focus on commute trips is also reflected in the multitude of person factors used to explain variation in travel behavior in past research (Table 1.1). Table 1.1 also shows household and location characteristics that have been considered as determinants of long distance travel behavior. It is also important to note that a few researchers (de Abreu et al., 2006, 2012) consider long distance travel, car ownership, and residential and job location (and the distance between the two) as a system of joint decisions that are best explained using methods that can disentangle the complex relationships among all these behavioral facets. From this viewpoint, long distance travel (particularly for commuters) cannot be separated from the choice of work and home location and should be modeled jointly. The review in Mitra (2016) is particularly useful in mapping recent literature on long distance travel and its determinants. His findings are exactly what one would expect: age, gender, education, employment and occupation, car ownership, household structure, place of residence and workplace as well as housing cost and accessibility influence long distance travel in a variety of ways. His analysis also shows that developing traveler profiles at the level of a household (rather than the individual) is a better choice to understand how and why long distance travel happens, and our analysis follows this lead. In another analysis of CHTS, Bierce and Kurth (2014) identified an issue of underreporting of repetitive trips in the 8-week long distance data. In essence, long distance commuters did not report all their commuting trips. We find that this underreporting may also exist for longer trips, though less severely than it does for shorter ones. Identifying the correct mix of distances and overall volume of travel is particularly important when one seeks to estimate the contribution of VMT from long distance travel to California estimates of VMT (see also Chapman, 2007).
This project developed a quantile regression method for predicting future traffic flow at a signalized intersection by combining both historical and real-time data. The algorithm exploits nonlinear correlations in historical measurements and efficiently solves a quantile loss optimization problem using the Alternating Direction Method of Multipliers (ADMM). The resulting parameter vectors allow determining a probability distribution of upcoming traffic flow. These predictions establish an efficient, delay-minimizing control policy for the intersection. The approach is demonstrated on a case study with two years of high resolution flow measurements. It is emphasized that the results are applicable to any traffic intersection equipped with sensors that provide sufficiently high resolution of data acquisition. In particular, the data must have sufficient spatial resolution, e.g., measuring turning counts, and sufficient temporal resolution, e.g., measurements each 15 minutes. For example, numerous sites in California, including a large number of intersections in LA County, possess sensors that provide the required data to a central server.
Mobile Apps and Transportation: A Review of Smartphone Apps and A Study of User Response to Multimodal Traveler Information
In recent years, technological and social forces have pushed smartphone applications (apps) from the fringe to the mainstream. Understanding the role of transportation apps in urban mobility is important for policy development and transportation planners. This study evaluates the role and impact of multimodal aggregators from a variety of perspectives, including a literature review; a review of the most innovative, disruptive, and highest-rated transportation apps; interviews with experts in the industry, and a user survey of former multimodal aggregator RideScout users. Between February and April 2016, researchers conducted interviews with experts to gain a stronger understanding about challenges and benefits of data sharing between private companies and public agencies. Key findings from the expert interviews include the critical need to protect user privacy; the potential to use data sharing to address integrated corridor and congestion management as well as various pricing strategies during peak hours; along with the potential benefits for improving coordination between the public and private sectors. In March 2016, researchers surveyed 130 people who had downloaded the RideScout app to evaluate attitudes and perceptions toward mobile apps, travel behavior, and modal shift. The goal was to enhance understanding of how the multimodal apps were impacting the transportation behavior. The survey did found that respondents used multimodal apps in ways that yielded travel that was less energy intensive and more supportive of public transit. Looking to the future, smartphone applications and more specifically multimodal aggregators, may offer the potential for transportation planners and policymakers to enhance their understanding of multimodal travel behavior, share data, enhance collaboration, and identify opportunities for public-private partnerships.
Estimating the producer surplus – the revenue above the average long-run cost – is an important part of social cost-benefit analyses of changes in petroleum use. This paper estimates the producer surplus associated with changes in gasoline fuel use in the United States, and then applies the estimates of producer surplus to two kinds of social cost-benefit analyses related to petroleum use: (1) estimating the wealth transfer from consumers to producers as a result of policies that affect oil use and oil imports to the US, and (2) comparing the actual average cost of gasoline with the average cost of environmentally superior alternatives to gasoline, such as hydrogen. Our results show that a 50% reduction in gasoline use in the US in 2004 would have saved the US $72 billion in producer surplus payments to foreign oil producers. Applying our estimates to the comparison of the social lifetime cost of hydrogen vehicles versus gasoline vehicles, we find that inconsistently counting producer surplus from a US national perspective while counting climate change damages from a global perspective can overstate the present value lifetime costs of gasoline vehicles by $2,200 to $9,800 per vehicle.
This paper presents a simple approximate procedure for traffic analysis that can be described geometrically without calculus. The procedure, which is graphically intuitive, operates directly on piecewise linear approximations of the N-curves of cumulative vehicle count. Because the N-curves are both readily observable and of direct interest for evaluation purposes (e.g., they yield the total vehicle-hours and vehicle-miles of travel in a time interval, and the vehicular accumulation as a function of time) the predictions made with this method should be practical and easy to test.
Travel demand forecasting models play an important role in guiding policy, planning, and design of transportation systems. There is no shortage of literature critiquing the accuracy of model forecasts (see, for example, Pickrell, 1989; Wachs, 1990; Pickrell, 1992; Flyvbjerg, Skamris Holm, and Buhl 2005; Richmond, 2005; Flyvbjerg, 2007; Bain, 2009; Parthasarathi and Levinson, 2010; Welde and Odeck, 2011; Hartgen, 2013; Nicolaisen and Driscoll, 2014; Schmitt, 2016; Odeck and Welde, 2017, and Voulgaris, 2019), not to mention several high-profile lawsuits (Saulwick 2014, Stacey 2015, Rubin 2018). Many researchers and practitioners feel more can be done to advance rigorous travel analysis methods for the public good (see, e.g., zephyrtransport.org). Motivated by these critiques, a two-day, NSF-funded workshop was held at UC Berkeley in the Spring of 2017 to engage in a fundamental review of the state of the art in travel demand modeling, to discuss the future of the field, and to propose new directions and processes for advancing the science.
Travel demand forecasting is an inherently practical enterprise. While academics drive the fundamental research, the users of travel demand models and forecasts are typically government agencies and transport operators that use the models to inform long-range investment, funding, and planning decisions. Private firms play a key role in assisting the agencies in both development and application of the models, and, more recently, high-tech firms have entered the development fray. While all of these actors have important roles in advancing the science of the field, in this report we focus our attention primarily on the academic side of the enterprise, consistent with the orientation of the funding agency (NSF), and in order to make the task manageable. That said, other sectors are represented in various parts of this report as they interface with academics or play particularly central roles in our proposals for advancing the science.
Trust and Compassion in Willingness to Share Mobility and Sheltering Resources in Evacuations: A case Study of the 2017 and 2018 California Wildfires
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.
Related Research Centers & Groups
- California Partners for Advanced Transportation Technology
- Safe Transportation Research & Education Center
- UC Berkeley Center for Future Urban Transport: A Volvo Center of Excellence
- UC Berkeley Transportation Sustainability Research Center
- UC Davis Institute of Transportation Studies
- UC Irvine Institute of Transportation Studies
- UCLA Institute of Transportation Studies
- University of California Institute of Transportation Studies