research report
Project included three major parts: 1) field measurements of particulate matter in five urban areas, 2) laboratory modeling of flow and dispersion within model urban areas, and 3) numerical modeling. Project website and database are located at http://emissions.engr.ucr.edu/.
research report |
Aug 2010
published journal article |
Aug 2011
published journal article |
Jan 2013
conference paper
conference paper
working paper
Proper prediction of traffic flow parameters is an essential component of any proactive traffic control system and one of the pillars of advanced management of dynamic traffic networks. In this paper, we present a new short term traffic flow prediction system based on an advanced Time Delay Neural Network (TDNN) model, the structure of which is optimized using a Genetic Algorithm (GA). After presentation of the model’s development, its performance is validated using both simulated and real traffic flow data obtained from the California Testbed in Orange County, California. The model predicts flow and occupancy values at a given freeway site based on contributions from their recent temporal profile as well the spatial contribution from neighboring sites. Both temporal and spatial effects were found essential for proper prediction. An in-depth investigation of the variables pertinent to traffic flow prediction was conducted examining the extent of the “look-back” interval, the extent of prediction in the future, the extent of spatial contribution, the resolution of the input data, and their effects on prediction accuracy. Results obtained indicate that the prediction errors vary inversely with the extent of the spatial contribution, and that the inclusion of three loop stations in both directions of the subject station is sufficient for practical purposes. Also, the longer the extent of prediction, the more the predicted values tend toward the mean of the actual, for which case the optimal look-back interval also shortens. Interestingly, it was found that coarser data resolution is better for longer extents of prediction. The implication is that the level of data aggregation/resolution should be comparable to the prediction horizon for best accuracy. The model performed acceptably using both simulated and real data. The model also showed potential to be superior to such other well-known neural network models as the Multi layer Feed-forward (MLF) when applied to the same problem. Keywords: Traffic Flow Prediction, Neural Networks, Genetic Algorithms, Traffic Management.
research report |
Sep 1993
working paper |
Nov 1995
research report |
Sep 1997
working paper |
Jun 1999
Phd Dissertation |
Jun 1996
published journal article
working paper
Travel demand is viewed as being derived from the demand for out-of-home activities. The journey to work can have a significant impact on the travel and activity patterns of workers and other household members. The objective of this research is to model the relationships between travel and activity participation and examine how these relationships are influenced by the time a worker spends commuting between home and his or her worksite. Causal hypotheses are tested using data from approximately 140 workers who responded to two waves of a panel survey collected as part of the State of California Telecommuting Pilot Project. These data contain detailed descriptions of all travel by the survey respondents over three working days in each of two years, 1988 and 1989. A structural equations model is specified in which the durations of four exhaustive categories of out-of-home activities – work, personal business, shopping and social/recreation -generate needs for time spent traveling, and durations and travel times are interrelated in a complex causal structure. The effects of the reduction in travel times for work by telecommuters in the second wave of the panel are captured in terms of additional structural parameters. Results indicate that telecommuting leads directly to increases in shopping activities and decreases in travel for social/recreational activities, and leads indirectly to changes in travel for all purposes. A general modeling framework in which activities and travel relationships can be studied is also discussed.
working paper |
Mar 1989
working paper |
Oct 1991
working paper |
Jun 1989
working paper |
Nov 1988
working paper |
Oct 1989
published journal article
Background Recent studies have reported inconsistent associations between maternal residential green space and preterm birth (PTB, born < 37 completed gestational weeks). In addition, windows of susceptibility during pregnancy have not been explored and potential interactions of green space with air pollution exposures during pregnancy are still unclear. Objectives To evaluate the relationships between green space and PTB, identify windows of susceptibility, and explore potential interactions between green space and air pollution. Methods Birth certificate records for all births in California (2001–2008) were obtained. The Normalized Difference Vegetation Index (NDVI) was used to characterized green space exposure. Gestational age was treated as a time-to-event outcome; Cox proportional hazard models were applied to estimate the association between green space exposure and PTB, moderately PTB (MPTB, gestational age < 35 weeks), and very PTB (VPTB, gestational age < 30 weeks), after controlling for maternal age, race/ethnicity, education, and median household income. Month-specific green space exposure was used to identify potential windows of susceptibility. Potential interactions between green space and air pollution [fine particulate matter < 2.5 µm (PM2.5), nitrogen dioxide (NO2), and ozone (O3)] were examined on both additive and multiplicative scales. Results In total, 3,753,799 eligible births were identified, including 341,123 (9.09%) PTBs, 124,631 (3.32%) MPTBs, and 22,313 (0.59%) VPTBs. A reduced risk of PTB was associated with increases in residential NDVI exposure in 250 m, 500 m, 1000 m, and 2000 m buffers. In the 2000 m buffer, the association was strongest for VPTB [adjusted hazard ratio (HR) per interquartile range increase in NDVI: 0.959, 95% confidence interval (CI): 0.942–0.976)], followed by MPTB (HR = 0.970, 95% CI: 0.962–0.978) and overall PTB (HR = 0.972, 95% CI: 0.966–0.978). For PTB, green space during the 3rd − 5th gestational months had stronger associations than those in the other time periods, especially during the 4th gestational month (NDVI 2000 m: HR = 0.970, 95% CI: 0.965–0.975). We identified consistent positive additive and multiplicative interactions between decreasing green space and higher air pollution. Conclusion This large study found that maternal exposure to residential green space was associated with decreased risk of PTB, MPTB, and VPTB, especially in the second trimester. There is a synergistic effect between low green space and high air pollution levels on PTB, indicating that increasing exposure to green space may be more beneficial for women with higher air pollution exposures during pregnancy.
working paper
Auto insurance rates can vary dramatically, with much higher premiums in poor and minority areas than elsewhere, even after accounting for individual characteristics, driving history and coverage. This project used a unique data set to examine the relative influence of place-based socioeconomic characteristics (or redlining) and place-based risk factors on the place-based component of automobile insurance premiums. We used a novel approach of combining tract-level census data and car insurance rate quotes from multiple companies for sub-areas within the city of Los Angeles. The quotes are for a hypothetical individual with identical demographic and auto characteristics, driving records and insurance coverage. This method allowed the individual demographic and driving record to be fixed. Multivariate models are then used to estimate the independent contributions of these risk and redlining factors to the place-based component of the car insurance premium. We find that both risk and redlining factors are associated with variations in insurance costs in the place-based component, with black and poor neighborhoods being adversely affected, although risk factors are stronger predictors. However, even after risk factors are taken into account in the model specification, SES factors remain statistically significant. Moreover, simulations show that redlining factors explain more of the gap in auto insurance premiums between black (and Latino) and white neighborhoods and between poor and nonpoor neighborhoods. The findings do not appear sensitive to the individual characteristics of the hypothetical driver.
research report
A simple, continuously collected GPS sequence was investigated to determine whether it can be used to accurately measure human behavior. Hybrid Dynamic Mixed Network (HDMN) modeling techniques were applied to learn behaviors given an extended GPS data stream. A key design decision behind the proposed architecture was to use an Enterprise Service Bus (ESB) to provide a communication infrastructure among various components of the application. Personal Travel Assistants running on mobile devices like cell phones could help travelers change their travel plans when routes are affected by crashes or natural disasters.
published journal article
<p><span id="page3R_mcid27" class="markedContent"><span dir="ltr" style="left: calc(var(–scale-factor)*100.34px); top: calc(var(–scale-factor)*263.71px); font-size: calc(var(–scale-factor)*8.04px); font-family: sans-serif; transform: scaleX(0.939886);" role="presentation">Markov processes (or Markov chains) are used for modeling a phenomenon in which changes over time of a random variable</span> <span dir="ltr" style="left: calc(var(–scale-factor)*100.34px); top: calc(var(–scale-factor)*272.95px); font-size: calc(var(–scale-factor)*8.04px); font-family: sans-serif; transform: scaleX(0.91935);" role="presentation">comprise a sequence of values in the future, each of which depends only on the immediately preceding state, not on other past</span> <span dir="ltr" style="left: calc(var(–scale-factor)*100.34px); top: calc(var(–scale-factor)*282.19px); font-size: calc(var(–scale-factor)*8.04px); font-family: sans-serif; transform: scaleX(0.915678);" role="presentation">states. A Markov process (PM) is completely characterized by specifying the finite set S of possible states and the stationary</span> <span dir="ltr" style="left: calc(var(–scale-factor)*100.34px); top: calc(var(–scale-factor)*291.31px); font-size: calc(var(–scale-factor)*8.04px); font-family: sans-serif; transform: scaleX(0.934392);" role="presentation">probabilities (i.e. time-invariant) of transition between these states.</span></span><span id="page3R_mcid28" class="markedContent"> <span dir="ltr" style="left: calc(var(–scale-factor)*318.67px); top: calc(var(–scale-factor)*291.31px); font-size: calc(var(–scale-factor)*8.04px); font-family: sans-serif; transform: scaleX(0.987133);" role="presentation">The</span></span><span id="page3R_mcid29" class="markedContent"></span><span id="page3R_mcid30" class="markedContent"> <span dir="ltr" style="left: calc(var(–scale-factor)*333.07px); top: calc(var(–scale-factor)*291.31px); font-size: calc(var(–scale-factor)*8.04px); font-family: sans-serif; transform: scaleX(0.952517);" role="presentation">software</span></span><span id="page3R_mcid31" class="markedContent"></span><span id="page3R_mcid32" class="markedContent"> <span dir="ltr" style="left: calc(var(–scale-factor)*362.47px); top: calc(var(–scale-factor)*291.31px); font-size: calc(var(–scale-factor)*8.04px); font-family: sans-serif; transform: scaleX(0.888688);" role="presentation">mos</span></span><span id="page3R_mcid33" class="markedContent"><span dir="ltr" style="left: calc(var(–scale-factor)*375.82px); top: calc(var(–scale-factor)*291.31px); font-size: calc(var(–scale-factor)*8.04px); font-family: sans-serif; transform: scaleX(0.879561);" role="presentation">t used</span></span><span id="page3R_mcid34" class="markedContent"></span><span id="page3R_mcid35" class="markedContent"> <span dir="ltr" style="left: calc(var(–scale-factor)*396.82px); top: calc(var(–scale-factor)*291.31px); font-size: calc(var(–scale-factor)*8.04px); font-family: sans-serif; transform: scaleX(0.937188);" role="presentation">in medical applications</span></span><span id="page3R_mcid36" class="markedContent"></span><span id="page3R_mcid37" class="markedContent"> <span dir="ltr" style="left: calc(var(–scale-factor)*473.02px); top: calc(var(–scale-factor)*291.31px); font-size: calc(var(–scale-factor)*8.04px); font-family: sans-serif; transform: scaleX(0.901263);" role="presentation">is</span></span><span id="page3R_mcid38" class="markedContent"></span><span id="page3R_mcid39" class="markedContent"> <span dir="ltr" style="left: calc(var(–scale-factor)*480.34px); top: calc(var(–scale-factor)*291.31px); font-size: calc(var(–scale-factor)*8.04px); font-family: sans-serif; transform: scaleX(0.935546);" role="presentation">produced by </span></span><span id="page3R_mcid40" class="markedContent"><span dir="ltr" style="left: calc(var(–scale-factor)*100.34px); top: calc(var(–scale-factor)*300.55px); font-size: calc(var(–scale-factor)*8.04px); font-family: sans-serif; transform: scaleX(0.969267);" role="presentation">TreeAge</span></span><span id="page3R_mcid41" class="markedContent"><span dir="ltr" style="left: calc(var(–scale-factor)*128.18px); top: calc(var(–scale-factor)*300.55px); font-size: calc(var(–scale-factor)*8.04px); font-family: sans-serif;" role="presentation">,</span></span><span id="page3R_mcid42" class="markedContent"> <span dir="ltr" style="left: calc(var(–scale-factor)*132.26px); top: calc(var(–scale-factor)*300.55px); font-size: calc(var(–scale-factor)*8.04px); font-family: sans-serif; transform: scaleX(0.942177);" role="presentation">since it offers</span></span><span id="page3R_mcid43" class="markedContent"></span><span id="page3R_mcid44" class="markedContent"> <span dir="ltr" style="left: calc(var(–scale-factor)*177.89px); top: calc(var(–scale-factor)*300.55px); font-size: calc(var(–scale-factor)*8.04px); font-family: sans-serif; transform: scaleX(0.933813);" role="presentation">many</span></span><span id="page3R_mcid45" class="markedContent"></span><span id="page3R_mcid46" class="markedContent"> <span dir="ltr" style="left: calc(var(–scale-factor)*197.69px); top: calc(var(–scale-factor)*300.55px); font-size: calc(var(–scale-factor)*8.04px); font-family: sans-serif; transform: scaleX(0.932563);" role="presentation">advantages to the user. But, the cost of the Treeage software is relatively high.</span></span><span id="page3R_mcid47" class="markedContent"> <span dir="ltr" style="left: calc(var(–scale-factor)*454.42px); top: calc(var(–scale-factor)*300.55px); font-size: calc(var(–scale-factor)*8.04px); font-family: sans-serif; transform: scaleX(0.964072);" role="presentation">Therefore in this</span></span><span id="page3R_mcid48" class="markedContent"> <span dir="ltr" style="left: calc(var(–scale-factor)*100.34px); top: calc(var(–scale-factor)*309.79px); font-size: calc(var(–scale-factor)*8.04px); font-family: sans-serif; transform: scaleX(0.927866);" role="presentation">article two software alternatives are presented: Sto Tree</span></span><span id="page3R_mcid49" class="markedContent"></span><span id="page3R_mcid50" class="markedContent"> <span dir="ltr" style="left: calc(var(–scale-factor)*280.63px); top: calc(var(–scale-factor)*309.79px); font-size: calc(var(–scale-factor)*8.04px); font-family: sans-serif; transform: scaleX(0.927152);" role="presentation">and</span></span><span id="page3R_mcid51" class="markedContent"></span><span id="page3R_mcid52" class="markedContent"> <span dir="ltr" style="left: calc(var(–scale-factor)*294.31px); top: calc(var(–scale-factor)*309.79px); font-size: calc(var(–scale-factor)*8.04px); font-family: sans-serif; transform: scaleX(0.88554);" role="presentation">the zero cost ad</span></span><span id="page3R_mcid53" class="markedContent"><span dir="ltr" style="left: calc(var(–scale-factor)*344.35px); top: calc(var(–scale-factor)*309.79px); font-size: calc(var(–scale-factor)*8.04px); font-family: sans-serif;" role="presentation">d</span></span><span id="page3R_mcid54" class="markedContent"><span dir="ltr" style="left: calc(var(–scale-factor)*348.43px); top: calc(var(–scale-factor)*309.79px); font-size: calc(var(–scale-factor)*8.04px); font-family: sans-serif;" role="presentation">-</span></span><span id="page3R_mcid55" class="markedContent"><span dir="ltr" style="left: calc(var(–scale-factor)*350.95px); top: calc(var(–scale-factor)*309.79px); font-size: calc(var(–scale-factor)*8.04px); font-family: sans-serif; transform: scaleX(0.957184);" role="presentation">in package "markovchain" implemented in R. An</span></span><span id="page3R_mcid56" class="markedContent"> <span dir="ltr" style="left: calc(var(–scale-factor)*100.34px); top: calc(var(–scale-factor)*318.91px); font-size: calc(var(–scale-factor)*8.04px); font-family: sans-serif; transform: scaleX(0.909639);" role="presentation">example of a cost</span></span><span id="page3R_mcid57" class="markedContent"><span dir="ltr" style="left: calc(var(–scale-factor)*156.62px); top: calc(var(–scale-factor)*318.91px); font-size: calc(var(–scale-factor)*8.04px); font-family: sans-serif;" role="presentation">-</span></span><span id="page3R_mcid58" class="markedContent"><span dir="ltr" style="left: calc(var(–scale-factor)*159.29px); top: calc(var(–scale-factor)*318.91px); font-size: calc(var(–scale-factor)*8.04px); font-family: sans-serif; transform: scaleX(0.932854);" role="presentation">effectiveness analysis of two possible treatments for advanced cervical cancer, previously conducted with the</span></span><span id="page3R_mcid59" class="markedContent"> <span dir="ltr" style="left: calc(var(–scale-factor)*100.34px); top: calc(var(–scale-factor)*328.15px); font-size: calc(var(–scale-factor)*8.04px); font-family: sans-serif; transform: scaleX(0.927157);" role="presentation">Treeage software, is re</span></span><span id="page3R_mcid60" class="markedContent"><span dir="ltr" style="left: calc(var(–scale-factor)*173.21px); top: calc(var(–scale-factor)*328.15px); font-size: calc(var(–scale-factor)*8.04px); font-family: sans-serif;" role="presentation">-</span></span><span id="page3R_mcid61" class="markedContent"><span dir="ltr" style="left: calc(var(–scale-factor)*175.85px); top: calc(var(–scale-factor)*328.15px); font-size: calc(var(–scale-factor)*8.04px); font-family: sans-serif; transform: scaleX(0.916132);" role="presentation">analyzed with these two low cost software packages.</span></span> <span id="page3R_mcid63" class="markedContent"><span dir="ltr" style="left: calc(var(–scale-factor)*100.34px); top: calc(var(–scale-factor)*337.27px); font-size: calc(var(–scale-factor)*8.04px); font-family: sans-serif; transform: scaleX(0.95059);" role="presentation">You can find a Spanish version of this paper in the following link: http://faculty.sites.uci.edu/lrkeller/publications</span></span></p>