Search

The popularity of working from home (WFH) in the US has surged over the past two decades, with the COVID‐19 pandemic further accelerating this trend. We hypothesize that WFH not only reduces the frequency of physical commutes but also lowers the time cost of commutes due to decreased urban congestion levels; both factors would flatten house‐price gradients. Analyzing big data from Google Maps on travel time in California, we first confirm that COVID‐19, as a WFH‐boosting shock, induced larger decreases in morning travel time in cities with a higher WFH potential. We then empirically validate the effect of WFH on house‐price gradients, channeled through its impact on commuting time; this effect explains 20% of the total WFH‐induced flattening of house‐price gradients during the pandemic in California. [ABSTRACT FROM AUTHOR]

Traffic congestion is ubiquitous in major cities around the world. Congestion is associated with a slew of negative effects, including delays and local air pollution. Because of the negative effects of congestion, governments invest billions of dollars into the highway system to try to reduce congestion and accommodate peak-hour automobile travel demand. The COVID-19 pandemic presented a significant disruption to transportation systems globally. One impact was a drastic reduction in travel, leading to free-flowing traffic conditions in many previously-congested cities. As lockdowns eased, traffic volumes returned to near-normal levels. However, the temporal pattern of demand may differ, due to increased remote work or other factors. In this article, we examine the temporal distribution of highway demand in California, using data from over 3,500 traffic sensors. We find that peak-hour automobile travel is spreading in the post-lockdown period. In addition to decreased traffic congestion, this finding also has implications for infrastructure investment. Roadways are generally sized based on peak-hour demand. As the peaks spread, some highway construction project may prove unnecessary. It may be possible to reallocate road space to other uses with fewer tradeoffs in terms of traffic congestion. [ABSTRACT FROM AUTHOR]

Secondary crashes (SCs) are typically defined as the crash that occurs within the spatiotemporal boundaries of the impact area of the primary crashes (PCs), which will intensify traffic congestion and induce a series of road safety issues. Predicting and analyzing the time and distance gaps between the SCs and PCs will help to prevent the occurrence of SCs. In this paper, a combined data-driven method of static and dynamic approaches is applied to identify SCs. Then, the random forests (RF) method is implemented to predict the two gaps using temporal, primary crash, roadway, and real-time traffic characteristics data collected from 2016 to 2019 at California interstate freeways. Subsequently, the SHapley Additive explanation (SHAP) approach is employed to interpret the RF outputs. The results show that the traffic volume, speed, lighting, and population are considered the most significant factors in both gaps. Furthermore, the main and interaction effects of factors are also quantified. High volume possibly promotes the time and distance gaps, while low volume inhibits them. And volume affects the distance gap inconsiderably when it falls between 300 and 400 veh/5 min. Traffic conditions with high speed and low volume are strongly associated with short-time and short-distance gaps. Darker surroundings probably accelerate the occurrence of SCs. Moreover, crashes involving the violation categories of improper turns or unsafe lane changes likely result in long time and distance gaps. These results have important implications for proposing traffic management and improving road safety. [ABSTRACT FROM AUTHOR]

Intelligent traffic control and guidance system is an effective way to solve urban traffic congestion, improve road capacity and guarantee drivers' travel safety, while short-term traffic flow prediction is the core of intelligent traffic control and guidance system. To investigate the long-term memory and the dynamic feature of short-time traffic flow time series, a hybrid model was proposed by integrating autoregressive fractionally integrated moving average (ARFIMA) model and nonlinear autoregressive (NAR) neural network model to predict short-time traffic flow, in which ARFIMA model can address the long-term memory of linear component and NAR neural network can accommodate the dynamic feature of nonlinear residual component. First, the ARFIMA model was employed to predict the linear component of traffic flow, and the results were compared with those of autoregressive integrated moving average (ARIMA) model. Next, the NAR neural network model was adopted to forecast the nonlinear residual components, and the weighted results were considered as the predicted flow of the hybrid model. The proposed hybrid model was validated by using the cross-sectional traffic flow data in California freeways obtained from the open-access PeMS database. The results showed that the ARFIMA model considering the long-term memory can effectively predict the short-term traffic flow, and the prediction accuracy of the hybrid model is better than that of the singular models. The findings provide an alternative for the short-term traffic flow prediction with lower error and higher accuracy. [ABSTRACT FROM AUTHOR]

Understanding the causal impact of meteorological variations on traffic conditions (e.g., traffic flow and speed) is crucial for effective traffic prediction and management, as well as the mitigation of adverse weather effects on traffic. However, many existing studies focused on establishing associations between meteorological situations and traffic, rather than delving into causal relationships, especially with deep learning techniques. Consequently, the ability to identify specific meteorological conditions that significantly contribute to traffic congestion or delays is still limited. To address this issue, this study proposes the M eteorological- T raffic C ausal I nference V ariational A uto- E ncoder Model (MT-CIVAE) to estimate the causal impact of fine-grained meteorological variations (e.g., rain and temperature) on traffic. Specifically, MT-CIVAE is based on the Variational Auto-Encoder and consists of an encoder to recover the distribution of latent confounders and a decoder to estimate the conditional probabilities of treatments. Transformer encoder layers are incorporated to analyze the spatial and temporal correlations of historical traffic data to further enhance the inference capability. To evaluate the effectiveness of the proposed approach for causal inference, real-world traffic flow and speed datasets collected from California, along with corresponding fine-grained meteorological datasets, are employed. The counterfactual analysis is conducted using artificially generated meteorological conditions as treatments, which allows for the simulation of hypothetical meteorological scenarios and the evaluation of their potential impact on traffic conditions. This study develops deep learning methods for assessing the causal impact of meteorological variations on traffic dynamics, offering explanations and insights that can assist transportation institutions in guiding post-meteorology traffic management strategies. • Develop causality-aware deep methods between meteorological variations and traffic dynamics. • The designed MT-CIVAE effectively captures complex spatiotemporal correlations. • The designed MT-CIVAE effectively mitigates influence of undetermined confounders. • Experiments have been conducted on real-world traffic flow and speed data from California. [ABSTRACT FROM AUTHOR]

The occurrence of secondary accidents leads to traffic congestion and road safety issues. Secondary accident prevention has become a major consideration in traffic incident management. This paper investigates the location and time of a potential secondary accident after the occurrence of an initial traffic accident. With accident data and traffic loop data collected over three years from California interstate freeways, a shock wave-based method was introduced to identify secondary accidents. A linear regression model and two machine learning algorithms, including a back-propagation neural network (BPNN) and a least squares support vector machine (LSSVM), were implemented to explore the distance and time gap between the initial and secondary accidents using inputs of crash severity, violation category, weather condition, tow away, road surface condition, lighting, parties involved, traffic volume, duration, and shock wave speed generated by the primary accident. From the results, the linear regression model was inadequate in describing the effect of most variables and its goodness-of-fit and accuracy in prediction was relatively poor. In the training programs, the BPNN and LSSVM demonstrated adequate goodness-of-fit, though the BPNN was superior with a higher CORR and lower MSE. The BPNN model also outperformed the LSSVM in time prediction, while both failed to provide adequate distance prediction. Therefore, the BPNN model could be used to forecast the time gap between initial and secondary accidents, which could be used by decision makers and incident management agencies to prevent or reduce secondary collisions. [ABSTRACT FROM AUTHOR]

• A lane-level VSL (LVSL) approach with actor-critic framework is proposed to provide differential VSL control. • A hybrid reward function is presented to adjust relationship between the reducing crash risk and improving traffic efficiency. • A twin delayed deep deterministic policy gradient (TD3)-based LVSL approach is established for the first time. • The TD3LVSL shows better performance than baseline models in terms of improving traffic safety and traffic efficiency. Variable speed limit (VSL) control plays a vital role in the emerging connected automated vehicle highway (CAVH) system, which can alleviate recurrent traffic congestion caused by capacity drop and accidents. However, the effect of the VSL on improving traffic efficiency is still controversial. It is necessary to study how to explore the potential benefits of the VSL by balancing its influence on reducing crash risk and enhancing traffic efficiency. To fill the technological gap above, this paper proposes a reinforcement learning-based lane-level VSL (LVSL) control approach to conduct refined traffic control on the mainlines. Firstly, an actor-critic framework is developed to generate and evaluate the discrete speed limits of each lane in continuous action space. To optimize traffic control performance, a hybrid reward function is developed by synchronously considering traffic safety and traffic efficiency of the bottleneck area. Then, to solve the overestimation bias problem of the actor-critic methods caused by function approximation error, a twin delayed deep deterministic policy gradient (TD3) method is introduced to train the framework of the LVSL method. Finally, a real-world recurrent bottleneck of the State Route 91 highway in California is simulated with consideration of the connected automated vehicles to examine the performance of the TD3-based LVSL (TD3LVSL) controller. The simulation results reveal that the proposed method is capable of reducing crash risk and improving traffic efficiency synchronously. Compared with the LVSL controller based on the deep deterministic policy gradient approach, the TD3LVSL controller shows better performance in terms of traffic safety and efficiency. These findings indicate that the proposed controller could contribute to future traffic control in a CAVH environment. [ABSTRACT FROM AUTHOR]

Congestion is universally unpopular, but is it always a problem? Are some places more “congestion-adapted” than others? Using data for Los Angeles, we examine whether the geographies of congestion and accessibility are distinct by mapping and describing them across neighborhoods. We then estimate a series of regression models of trip-making to test the net effects of traffic delays on behavior. We find that there are places where people make many trips and engage in many activities despite lots of congestion, which tend to be more central, built-up areas that host many short trips; in other places, high congestion and low activity coincide. Why the variance? While congestion can constrain mobility and reduce accessibility, traffic is also associated with agglomerations of activity and is thus a byproduct of proximity-based accessibility. Whether agglomeration and congestion have net positive or negative impacts on activity participation thus varies substantially over space. Controlling for factors such as income and working at home, we find that the effects of congestion on access depend on whether congestion-adaptive travel choices (such as walking and making shorter trips to nearby destinations) are viable. Because “congestion-adapted” places tend to host more trip-making, planners may be justified in creating more such places in order to increase accessibility, even if doing so makes absolute levels of congestion worse in the process. [ABSTRACT FROM AUTHOR]

BACKGROUND: Ambient air pollution and tuberculosis (TB) have an impact on public health worldwide, yet associations between the two remain uncertain. OBJECTIVE: We determined the impact of residential traffic on mortality during treatment of active TB. METHODS: From 2000-2012, we enrolled 32,875 patients in California with active TB and followed them throughout treatment. We obtained patient data from the California Tuberculosis Registry and calculated traffic volumes and traffic densities in 100- to 400-m radius buffers around residential addresses. We used Cox models to determine mortality hazard ratios, controlling for demographic, socioeconomic, and clinical potential confounders. We categorized traffic exposures as quintiles and determined trends using Wald tests. RESULTS: Participants contributed 22,576 person-years at risk. There were 2,305 deaths during treatment for a crude mortality rate of 1,021 deaths per 10,000 person-years. Traffic volumes and traffic densities in all buffers around patient residences were associated with increased mortality during TB treatment, although the findings were not statistically significant in all buffers. As the buffer size decreased, fifth-quintile mortality hazards increased, and trends across quintiles of traffic exposure became more statistically significant. Increasing quintiles of nearest-road traffic volumes in the 100-m buffer were associated with 3%, 14%, 19%, and 28% increased risk of death during TB treatment [first quintile, referent; second quintile hazard ratio (HR) = 1.03 [95% confidence interval (CI): 0.86, 1.25]; third quintile HR = 1.14 (95% CI: 0.95, 1.37); fourth quintile HR=1.19 (95% CI: 0.99, 1.43); fifth quintile HR = 1.28 (95% CI: 1.07, 1.53), respectively; p-trend = 0.002]. CONCLUSIONS: Residential proximity to road traffic volumes and traffic density were associated with increased all-cause mortality in patients undergoing treatment for active tuberculosis even after adjusting for multiple demographic, socioeconomic, and clinical factors, suggesting that TB patients are susceptible to the adverse health effects of traffic-related air pollution. [ABSTRACT FROM AUTHOR]

We link daily air pollution exposure to measures of contemporaneous health for communities surrounding the twelve largest airports in California. These airports are some of the largest sources of air pollution in the US, and they experience large changes in daily air pollution emissions depending on the amount of time planes spend idling on the tarmac. Excess airplane idling, measured as residual daily taxi time, is due to network delays originating in the Eastern US. This idiosyncratic variation in daily airplane taxi time significantly impacts the health of local residents, largely driven by increased levels of carbon monoxide (CO) exposure.We use this variation in daily airport congestion to estimate the population dose-response of health outcomes to daily CO exposure, examining hospitalization rates for asthma, respiratory, and heart-related emergency room admissions.Aone standard deviation increase in daily pollution levels leads to an additional $540 thousand in hospitalization costs for respiratory and heart-related admissions for the 6 million individuals living within 10 km (6.2 miles) of the airports in California. These health effects occur at levels of CO exposure far below existing Environmental Protection Agency mandates, and our results suggest there may be sizable morbidity benefits from lowering the existing CO standard. [ABSTRACT FROM AUTHOR]

Public transit accounts for 1 percent of US passenger miles traveled but attracts strong public support. Using a simple choice model, we predict that transit riders are likely to be individuals who commute along routes with severe roadway delays. These individuals' choices thus have high marginal impacts on congestion. We test this prediction with data from a strike in 2003 by Los Angeles transit workers. Estimating a regression discontinuity design, we find that average highway delay increases 47 percent when transit service ceases. We find that the net benefits of transit systems appear to be much larger than previously believed. (JEL H76, J52, L92, R41) [ABSTRACT FROM AUTHOR]

Particle flows in spatial networks are susceptible to congestion. In this paper, we analyze the phase transitions of these networks to a state of congested transport and the influence of both topology and spatial dynamics on its emergence. We systematically show that the value of the critical loading rate at which congestion emerges is affected by the addition of spatial dynamics, changing the nature of this transition from a continuous to a discontinuous one. Our numerical results are confirmed by introducing an analytical solvable framework. As a case of study, we explore the implications of our findings in the San Francisco road network where we can locate the roads that originate the congested phase. These roads are spatially constrained, and not necessarily those with high betweenness as predicted by models without spatial dynamics. [ABSTRACT FROM AUTHOR]

Abstract: Over the past twenty-five years, more workers in the United States have been given the option of flexible work schedules, which are designed to redistribute commuter traffic over the course of a day by allowing employees to vary their arrival and/or departure times. This paper examines whether and to what degree access to flexible work schedules affects the departure times of commuters in the two largest and most congested areas of California: the Los Angeles and San Francisco regions. Trip data were obtained from the 2009 US National Household Travel Survey (NHTS). The results of this study show that people who have access to flexitime preferred later departure times rather than earlier times. Workers with flexible schedules were 3.30% less likely to depart before peak hours, 4.11% less likely during peak hours, and 7.41% more likely afterwards. Based on these findings, government agencies and private firms in regions with severe traffic congestion problems may consider adopting alternative work schedules. [Copyright &y& Elsevier]

EXECUTIVE SUMMARY: During the twentieth century, the private automobile emerged as the dominant mode of transportation access for visitors to travel to and through most U.S. national parks. Public land management agencies including the National Park Service increasingly are concerned about the relationship between park transportation systems and the quality of visitors' experiences and the natural environment. Among these concerns are visitor crowding, traffic congestion, parking shortages, air pollution, noise pollution, and impacts to wildlife and roadside vegetation. To address these concerns, national parks are implementing integrated transportation systems that combine traditional and alternative transportation modes with adaptive management strategies. Given the sharpening focus on transportation by national park researchers and managers, it is critical to understand visitors' travel-related attitudes, decision-making behavior, and experiences. Doing so would allow the national parks to develop transportation management policies and inform efforts to influence visitor behavior such as encouraging a shift to alternative transportation in the most effective and least disruptive way. This study builds on prior interpretive and qualitative research into visitors' experience of the transportation system in Yosemite National Park. The objectives of the current study are to: a) document travel mode choices for visitors entering and travelling through Yosemite National Park, b) identify the importance of transportation modes to visitors and their satisfaction with each mode, c) examine visitors' perceptions of experiential dimensions of travelling via alternative and traditional transportation modes in the park, and d) identify visitors' preferences regarding transportation management. Data were collected from a random sample of adult park visitors through an on-site, self-administered questionnaire (N=533; acceptance rate 73%). Findings indicate an enduring reliance on private automobiles as the primary mode for travel to and through the park. Visitors rated the ability to use alternative transportation inside the park as very important and visitors are satisfied with the convenience of alternative transportation modes. Visitors are also satisfied overall with the convenience of private vehicles. The results show substantively small but statistically significant differences in visitors' ratings of private automobiles versus park shuttle busses in terms of perceived sense of stress and conflict, freedom and access, and nature experience. These findings have implications for the development of indicators of transportation experience quality in national parks. [ABSTRACT FROM AUTHOR]

This paper analyzes urban multimodal transportation systems in an aggregated way. To describe the aggregate behavior of traffic in cities, use is made of an idea that is now receiving some attention: the macroscopic fundamental diagram (MFD). We demonstrate through simulation how the MFD can be used to monitor and control a real network, in this case a portion of San Francisco, using readily available input data. We then show how different modes interact on the same network and discuss how these interactions might be incorporated into an MFD for multimodal networks. The work unveils two main results: first, it confirms recent results showing that restricting access to a city's congested areas can improve mobility for all travelers, including those who endure the restrictions; and second, that dedicating street space to collective transport modes can improve accessibility for all modes, even those from which space is taken away. [ABSTRACT FROM AUTHOR]

Abstract: In recent years, the reduction of freight vehicle trips during peak hours has been a common policy goal. To this end, policies have been implemented to shift logistics operations to nighttime hours. The purpose of such policies has generally been to mitigate congestion and environmental impacts. However, the atmospheric boundary layer is generally more stable during the night than the day. Consequently, shifting logistics operations to the night may increase 24-h average concentrations of diesel exhaust pollutants in many locations. This paper presents realistic scenarios for two California cities, which provide diesel exhaust concentration and human intake estimates after temporal redistributions of daily logistics operations. Estimates are made for multiple redistribution patterns, including from 07:00–19:00 to 19:00–07:00, similar to daytime congestion charging polices, and from 03:00–18:00 to 18:00–03:00, corresponding to the PierPASS program at the ports of Los Angeles and Long Beach. Results for these two redistribution scenarios indicate that 24-h average exhaust concentrations would increase at most locations in California, and daily human intake is likely to worsen or be unimproved at best. These results are shown to be worse for inland than coastal settings, due to differences in meteorology. Traffic congestion effects are considered, using a new graphical method, which depicts how off-peak policies can be environmentally improving or damaging, depending on traffic speeds and meteorology. [ABSTRACT FROM AUTHOR]

Access to parks and recreational facilities, as well as their quality, congestion, and affordability, relates to physical activity levels and, in turn, various health outcomes. Yet park and recreational resources are often insufficient and unevenly distributed within metropolitan regions. This paper documents and analyzes the allocation of federal, state, special district, municipal, and nonprofit resources to parks and recreation facilities across municipalities within the Los Angeles region. Results indicate that local funding for parks and recreation resources is highly uneven (from less than 1 to over 500 per capita annually). Because most park and recreation spending originates at the local level, fiscally healthier cities allocate more resources to these uses. State and nonprofits funds tend to favor middle-income communities and fail to equalize spending. Inner-ring suburbs with large minority populations, and low-income exurban communities are most likely to suffer from low expenditure. Federal devolution, increased localization of service provision, and limited intergovernmental transfers have resulted in large intrametropolitan disparities in the distribution of resources for parks and recreation, ultimately shaping the landscape of health risks for local populations. [ABSTRACT FROM AUTHOR]

Abstract: This paper shows that a macroscopic fundamental diagram (MFD) relating average flow and average density must exist on any street with blocks of diverse widths and lengths, but no turns, even if all or some of the intersections are controlled by arbitrarily timed traffic signals. The timing patterns are assumed to be fixed in time. Exact analytical expressions in terms of a shortest path recipe are given, both, for the street’s capacity and its MFD. Approximate formulas that require little data are also given. For networks, the paper derives an upper bound for average flow conditional on average density, and then suggests conditions under which the bound should be tight; i.e., under which the bound is an approximate MFD. The MFD’s produced with this method for the central business districts of San Francisco (California) and Yokohama (Japan) are compared with those obtained experimentally in earlier publications. [Copyright &y& Elsevier]

Abstract: Smart growth and transit-oriented development proponents advocate increasing the density of new land development and infill redevelopment. This is partly in order to reduce auto use, by reducing distances between trip origins and destinations, creating a more enjoyable walking environment, slowing down road travel, and increasing the market for transit. But research investigating how development density influences household travel has typically been inadequate to account for this complex set of hypotheses: it has used theoretically unjustified measures, has not accounted for spatial scale very well, and has not investigated potentially important combinations of measures. Using data from a survey of metropolitan households in California, measures of development density corresponding to the main hypotheses about how density affects travel—activity density affecting distance traveled, network load density affecting the speed of auto travel, and built form density affecting the quality of walking—are tested as independent variables in models of auto trip speed and individual non-work travel. Residential network load density is highly negatively correlated with the speed of driving, and is also highly correlated with non-work travel, both singly and in combination with other measures. Activity density and built form density are not as significantly related, on their own. These results suggest that denser development will not influence travel very much unless road level-of-service standards and parking requirements are reduced or eliminated. [Copyright &y& Elsevier]

This article presents information about the FasTrak electronic toll collection system that is being installed on the bridges in the San Francisco Bay Area. It has been projected that the area will add nearly 2 million new residents between 2000 and 2030. Traffic is already tremendously congested on the bridges and will only get worse if the projections are correct. Automated toll-paying is one solution to congestion avoidance at toll plazas. FasTrak is the electronic toll collection system used on eight bridges to help the traffic move more quickly, but less than half of drivers use the system. The Bay Area Transportation Authority needs to find ways to promote more usage of the system by drivers.

Abstract: In this paper we investigate the effect of the Los Angeles transit strike on highway congestion through analysis of highway sensor data, using both a before-and-after comparison, and a control group comparison. We found that average 5:00 a.m. to 10:00 p.m. traffic speeds declined by as much as 20% during the strike, and the average length of the rush period increased by as much as 200%, even though increases in traffic were small, despite the fact that transit riders constitute a small fraction of the traveling population. Speeds declined the most at locations upstream from the places where queues normally end. We believe that highways are especially susceptible to congestion during strikes because travelers have little opportunity to adjust and equilibrate their travel patterns, as is possible during ordinary periods of traffic growth. [Copyright &y& Elsevier]

In the face of growth pressures, traffic congestion, and air quality nonconformity, jurisdictions throughout the state of California are increasingly planning for high-density transit-oriented development (TOD). This paper explores the extent to which these transit and station area investments are impacting transit ridership among those living and working in rail-based TODs. It also explores the relative roles of physical characteristics (e.g., pedestrian friendliness, street connectivity land-use mix), transportation-related policies (e.g., parking pricing and availability, telecommuting and flexible hours at the workplace, employer-subsidized travel costs), and regional transportation factors (e.g., job accessibility via the highway) in predicting transit use among TOD residents and office employees. Characteristics at both the trip origin amid trip destination are considered. The study builds upon a similar California study conducted one decade earlier and finds that, while TODs do confer meaningful ridership benefits, complementary policies and programs may be necessary before hoped-for ridership gains will be met. [ABSTRACT FROM AUTHOR]

Abstract: This study presents a real-time crash prediction model and uses this model to investigate the effect of the local traffic-responsive ramp metering strategy on freeway safety. Safety benefits of ramp metering are quantified in terms of the reduced crash potential estimated by the real-time crash prediction model. Driver responses to ramp metering and the consequent traffic flow changes were observed using a microscopic traffic simulation model and crash potential was estimated for a 14.8km section of I-880 in Hayward, California and a hypothetical isolated on-ramp network. The results showed that ramp metering reduced crash potential by 5–37% compared to the no-control case. It was found that safety benefits of local ramp metering strategy were only restricted to the freeway sections in the vicinity of the ramp, and were highly dependent on the existing traffic conditions and the spatial extent over which the evaluation was conducted. The results provide some insight into how a local ramp metering strategy can be modified to improve safety (by reducing total crash potential) on longer stretch of freeways over a wide range of traffic conditions. [Copyright &y& Elsevier]

This paper presents an empirical application of a congestion–alleviation strategy that C. Daganzo [Transportation Research B 29 (1995) 139–154] proposed as a “hybrid between rationing and pricing”. This strategy is applied to the San Francisco Bay Bridge corridor, in search of a practical and Pareto-improving solution to the Bridge''s congestion. The work relies on a mode-split model for work trips across four different income groups residing in 459 origin zones, and it applies an equilibrium analysis based on Bridge performance. Results indicate that modal utilities (and thus choices) are sensitive to the specific combination of toll and rationing rate, as well as to the Bridge''s travel-time (or performance) function, and the length of the congested section. Though no combination of tolls plus rationing rates was found to benefit all groups of travelers studied, further investigations may improve upon these results by refining some of the assumptions made here. [Copyright &y& Elsevier]

Explores whether childhood residence near roads with high traffic flow was associated with asthma in a low-income population in San Diego County, California. Use of geographic information systems and data from Medicaid claims; Relationship between traffic flow and medical care visits.

This article presents results of a survey of freight operating companies of California conducted in 1998, revealing the perceptions of freight operators about congestion problems and the application of advanced technologies. The value of total freight shipments originating in California in 1997 is estimated at $638.5 billion. The survey was carried out by a private survey research company for the Institute of Transportation Studies at the University of California in Irvine. The sample of 12,00 freight operators was drawn randomly from a set of 5,258 operators. The survey was conducted as a computer-aided telephone interview. Results lend insight into the primary causes and impacts of congestion on trucking operations, carriers' perceptions of the potential benefits of congestion mitigation policies, the extent of technology use in the industry, and the state of inter-modal freight terminals across California. It was also viewed that the vast majority of carriers perceive traffic congestion worsening in the next few years.

Bus systems in small California cities are widely believed to produce air quality, energy conservation, and traffic congestion benefits by inducing car-to-transit mode shifts. This paper tests the validity of these claims for a conventional bus system in Davis, California. Results from the Davis case fail to support claims for transit's benefits. The paper concludes that questions raised by the unexpected effects of transit in Davis and the lack of adequate data to evaluate similar systems in other cities should be addressed by collecting and analyzing additional data. Otherwise, transportation planners and public officials may be misled by inadequate or invalid information when formulating transportation policy or committing public resources to conventional bus systems. [ABSTRACT FROM AUTHOR]

To relieve worsening congestion and to accommodate new growth, many municipalities and counties across the country are turning to the private sector to finance new highways and general road improvements. California has probably accumulated more experience with financing areawide road improvements through special assessments, impact fees, and negotiated exactions than any other state. This paper reviews California's experiences with formulating and administering these programs based on a recent survey of municipal and county officials. A variety of approaches are used to set fees and payment schedules, with most jurisdictions charging developers a flat fee based on the amount of traffic that their projects are expected to generate. In general, those in charge of administering these programs recommend using pre-established formulas and consistent standards, as opposed to ad hoc negotiations, to exact payments from developers. [ABSTRACT FROM AUTHOR]

• A hybrid observer merging PWSL modeling and Kalman filter estimator is proposed • Two new kNN-based mechanisms for detecting road traffic congestion are designed. • Measurements from the four-lanes SR-60 freeway in California are used for validation. • The detection results show the superior performance of the new coupled kNN-ES mechanism. A systematic approach for monitoring road traffic congestion is developed to improve safety and traffic management. To achieve this purpose, an improved observer merging the benefits of a piecewise switched linear traffic (PWSL) modeling approach and Kalman filter (KF) is introduced. The PWSL-KF observer is utilized as a virtual sensor to emulate the traffic evolution in free-flow mode. In the proposed approach, residuals from the PWSL-KF model are used as the input to k-nearest neighbors (kNN) schemes for congestion detection. Here, kNN-based Shewhart and exponential smoothing schemes are designed for discovering the traffic congestions. The proposed detectors merge the desirable properties of kNN to appropriately separating normal from abnormal features and the capability of the monitoring schemes to better identify traffic congestions. In addition, kernel density estimation has been utilized to set nonparametric control limits of the proposed detectors and compared them with their parametric counterparts. Tests on traffic measurements from the four-lane State Route 60 in California freeways show the effectiveness of the PWSL-KF-based kNN methods in supervising traffic congestions. [ABSTRACT FROM AUTHOR]