published journal article
published journal article
The authors propose PEXIC (Priced EXchanges in Intersection Control), a new concept and algorithm for traffic signal control that incorporates user heterogeneity on value of delay savings. The algorithm assigns phases with associated delays, taking into account the vehicle travelersâ?? values for experienced delay. Applying principles of envy-freeness, the authors develop a pricing scheme that addresses fairness by minimizing user envy via compensatory monetary transfers among users. PEXIC is Pareto efficient and budget balanced, and thus financially self-sustainable without external subsidy. The optimization is solved sequentially on a rolling horizon basis: first the phasing, and next the pricing. PEXIC achieves significant cost reductions for a large range of volumes and usersâ?? value heterogeneity levels. Inclusion of user heterogeneity also proved to be fairer than standard delay minimization that disregards individual vehiclesâ?? values for delay savings. Furthermore, the authors show that arbitrage is not possible, thus there are no incentives to drive just to collect those payments. The method used has polynomial complexity and it is suitable for real-world implementation.
conference paper
conference paper
We analyze the impact of state and local incentives on household ownership of hybrid electric vehicles (HEVs) while accounting for their socio-economic characteristics. Our large dataset combines household data from the 2009 National Household Travel Survey (NHTS) with zip code-level retail gas prices and congestion data from the Texas Transportation Institute, as well as information about state and city incentives. These incentives include unrestricted HOV access, income tax credit, sales tax rebates, and city parking privileges. By contrast, studies published so far focused on just one or two incentives and/or they analyzed only their local impact. We find that unrestricted access to HOV lanes paired with a parking incentive or sales tax rebates/exemptions can effectively promote household ownership of HEVs; by contrast, state income tax credits do not appear to have a statistically significant impact. Moreover, our results indicate that households are more likely to purchase HEVs in areas where gasoline prices are higher, which suggests that increasing gasoline tax could effectively promote HEVs. Understanding the effectiveness of various government policies is important at a time when there is renewed interest in promoting HEVs to address our dependence on foreign oil, air pollution and global warming.
Phd Dissertation
Traffic safety is a major public health issue worldwide. The Kingdom of Saudi Arabia (KSA) is facing alarming road safety concerns with traffic-related injuries being the third leading cause of fatalities in the country. A better understanding of factors influencing injury severity outcomes of traffic crashes is vital for the proactive and effective implementation of suitable countermeasures. To address the drawbacks of statistical models lately, the application of various non-parametric machine learning (ML) based techniques have been increasingly used for modeling the injury severity of traffic crashes. The dissertation proposes the application of six different ML algorithms for injury severity prediction of traffic crashes based on three years data on interstate rural highways in KSA. Injury severity predictive performance of proposed algorithms was assessed in terms of various evaluation indices. Experimental results revealed that some models outperformed others based on the selected evaluation metrics. To address the ML model’s interpretability issue, feature importance and SHAP analysis were also employed. An analysis of several significant factors which aggravate the chances of fatal and severe injuries was done. The study also proposes the application of the Information Root Node Variation (IRNV) technique for extraction of significant decision rules highlighting the circumstances for the categorization of specific crash injury severity instances. For comparison purposes, multinomial logit (MNL) models were also developed to investigate the consistency of injury severity risk factors between MNL and ML models. The outcomes and findings of the current study can yield valuable insights to safety practitioners for the timely and effective implementation of suitable mitigation measures.
working paper
The San Pedro Bay Ports (SPBP) of Los Angeles and Long Beach in Southern California comprise one of the largest container port complexes in the world. The SPBP contribute significantly to both regional and national economies in California, and the US, respectively. However, the ongoing growth and economic benefits of the SPBP are threatened by negative externalities associated with port operations, particularly increasing congestion and air pollution. The objective of this paper is to explore a new approach to estimating vehicle emission impacts of freight corridor operations related to the port area, particularly those associated with heavy duty diesel trucks. The approach involves use of a microscopic traffic simulation model to capture detailed vehicle trajectories and congestion effects (ultimately including the effects of Intelligent Transportation System strategies), emissions modeling, and modeling the spatial dispersion of pollutants in the corridor, to facilitate estimation of the health and environmental justice impacts of freight corridor operations. In this paper we focus on operation of the I-710 freeway in the Alameda Corridor, leading from the SPBP area for about 20 miles toward Los Angeles. In a parallel effort we are also studying rail operations in the same corridor. In the future both the rail and highway elements will be combined to form an integrated, overall assessment of air quality impacts in the corridor. In this paper, seven scenarios were evaluated in addition to the 2005 Base Scenario: replacement of the current fleet of port heavy duty diesel trucks with zero emission trucks (25%, 50%, and 100% of port trucks), elimination of port heavy duty diesel truck trips (25%, 50%, and 100% reductions) that would correspond to shifting more containers to other modes such as rail, and implementation of a truck restricted-lane on I-710 preventing trucks from using the left most lanes. The results show that fleet replacement with cleaner trucks yields the most emission reductions both quantitatively and spatially.
published journal article
Phd Dissertation
This dissertation examines the relationship between transportation access and industrial and office property rents. The primary purpose of this research is to evaluate two sparsely studied topics in the transportation-land use literature: the impacts of light rail transit on property values, and the effect of transportation facilities on non-residential land uses. Multivariate regression analysis is used on longitudinal data for approximately five hundred and twenty office properties and five hundred industrial properties collected from the San Diego metropolitan region over the period from 1986 to 1995. Asking rents ($/square foot/month) is the dependent variable. Straight-line distance of each property to the nearest freeway on/off ramp, the nearest light rail station, and to the San Diego central business district provide measures of access. Other independent variables include building and neighborhood characteristics. The findings show that access to freeways is consistently significant in predicting office rents. This result indicates that freeways are important in shaping office property values, and by extension office land use patterns. Light rail transit did not have a significant effect on office rents. Access to the CBD was only significant for downtown office properties. The CBD variable in this case may be a proxy for the effect of localization economies. None of the measures of access was significant for industrial properties. This research underscores the importance of refining measures of access in order to capture and better understand the transportation-land use relationship. In particular, if the distance of an industrial firm to freeways, light rail transit, and the CBD is not important, then what kinds of access do matter? This research also has important implications for planning light rail transit systems. There is strong evidence that light rail systems do not provide enough travel cost savings to increase non-residential property values. This finding should be taken seriously in planning alignments for future light rail systems. Light rail systems need to be aligned with existing activity centers, rather than expected to stimulate new development or the redevelopment of distressed urban areas.
working paper |
Jan 1999
book/book chapter |
Oct 1998
working paper |
Sep 1994
working paper |
Dec 1994
working paper
We compare multinomial logit and mixed logit models for data on California households’ revealed and stated preferences for automobiles. The stated preference (SP) data elicited households’ preferences among gasoline, electric, methanol, and compressed natural gas vehicles with various attributes. The mixed logit models provide improved fits over logit that are highly significant, and show large heterogeneity in respondents’ preferences for alternative-fuel vehicles. The effects of including this heterogeneity are demonstrated in forecasting exercises. The alternative-fuel vehicle models presented here also highlight the advantages of merging SP and revealed preference (RP) data. RP data appear to be critical for obtaining realistic body-type choice and scaling information, but they are plagued by multicollinearity and difficulties with measuring vehicle attributes. SP data are critical for obtaining information about attributes not available in the marketplace, but pure SP models with these data give implausible forecasts.
conference paper
In this study, a comprehensive framework for forecasting a time-dependent future traffic pattern is introduced. In transportation planning, the future travel demand is calculated based on the change of socio-economic variables at a target year. In the forecasting process, however, the physical capacity of road network does not be considered explicitly, so a traffic simulation model might fail to load the forecasted OD demand table. Therefore, forecasting a dynamic future traffic pattern is a very difficult task with existing methods and models. In order to overcome the obstacle, the authors develop a comprehensive traffic simulator in which the supply side and the demand side of transportation system are modeled within a unified framework. The developed model relies on an activity-based approach and the household interactions among household members are also considered. A numerical example shows the developed model can find an equilibrium point between the dynamic travel demand and network supply.
