Keechoo Choi

Macroscopic spatial analysis of pedestrian and bicycle crashes

This study investigates the effect of spatial correlation using a Bayesian spatial framework to model pedestrian and bicycle crashes in Traffic Analysis Zones (TAZs). Aggregate models for pedestrian and bicycle crashes were estimated as a function of variables related to roadway characteristics, and various demographic and socio-economic factors. It was found that significant differences were present between the predictor sets for pedestrian and bicycle crashes. The Bayesian Poisson-lognormal model accounting for spatial correlation for pedestrian crashes in the TAZs of the study counties retained nine variables significantly different from zero at 95% Bayesian credible interval. These variables were - total roadway length with 35 mph posted speed limit, total number of intersections per TAZ, median household income, total number of dwelling units, log of population per square mile of a TAZ, percentage of households with non-retired workers but zero auto, percentage of households with non-retired workers and one auto, long term parking cost, and log of total number of employment in a TAZ. A separate distinct set of predictors were found for the bicycle crash model. In all cases the Bayesian models with spatial correlation performed better than the models that did not account for spatial correlation among TAZs. This finding implies that spatial correlation should be considered while modeling pedestrian and bicycle crashes at the aggregate or macro-level.

A study on crashes related to visibility obstruction due to fog and smoke.

Research on weather effects has focused on snow- or rain-related crashes. However, there is a lack of understanding of crashes that occur during fog or smoke (FS). This study presents a comprehensive examination of FS-related crashes using crash data from Florida between 2003 and 2007. A two-stage research strategy was implemented (1) to examine FS-related crash characteristics with respect to temporal distribution, influential factors and crash types and (2) to estimate the effects of various factors on injury severity given that a FS-related crash has occurred. The morning hours from December to February are the prevalent times for FS-related crashes. Compared to crashes under clear-visibility conditions, FS-related crashes tend to result in more severe injuries and involve more vehicles. Head-on and rear-end crashes are the two most common crash types in terms of crash risk and severity. These crashes were more prevalent on high-speed roads, undivided roads, roads with no sidewalks and two-lane rural roads. Moreover, FS-related crashes were more likely to occur at night without street lighting, leading to more severe injuries.

A DATA FUSION ALGORITHM FOR ESTIMATING LINK TRAVEL TIME

The growing demand for real-time traffic information brought about various types of traffic collection mechanisms in the area of Intelligent Transport Systems (ITS). There are, however, two procedures in making various traffic data into information. First, a robust information-making process of utilizing data into the representative information for each traffic collection mechanism is required. Second, the integration process of fusing the “estimated” information into the “representative information” for each link out of each source is also required. That is, both data reduction and/or data-to-information process and a higher-level information fusion are required. This article focuses on the development of an information fusion algorithm based on a voting technique, fuzzy regression, and Bayesian pooling technique for estimating dynamic link travel time in congested urban road networks. The algorithm has been proposed and validated using field experimental data—GPS probes and detector data collected over various roadway segments. It has been found that the estimated link travel time from the proposed algorithm is more accurate than the mere arithmetic mean counterpart from each traffic source. The limitations of the algorithm and future research agenda have also been discussed.

Development of accident prediction models for rural highway intersections

A study was done to develop macrolevel crash prediction models that can be used to understand and identify effective countermeasures for improving signalized highway intersections and multilane stop-controlled highway intersections in rural areas. Poisson and negative binomial regression models were fit to intersection crash data from Georgia, California, and Michigan. To assess the suitability of the models, several goodness-of-fit measures were computed. The statistical models were then used to shed light on the relationships between crash occurrence and traffic and geometric features of the rural signalized intersections. The results revealed that traffic flow variables significantly affected the overall safety performance of the intersections regardless of intersection type and that the geometric features of intersections varied across intersection type and also influenced crash type.

Multi-level hot zone identification for pedestrian safety.

According to the National Highway Traffic Safety Administration (NHTSA), while fatalities from traffic crashes have decreased, the proportion of pedestrian fatalities has steadily increased from 11% to 14% over the past decade. This study aims at identifying two zonal levels factors. The first is to identify hot zones at which pedestrian crashes occurs, while the second are zones where crash-involved pedestrians came from. Bayesian Poisson lognormal simultaneous equation spatial error model (BPLSESEM) was estimated and revealed significant factors for the two target variables. Then, PSIs (potential for safety improvements) were computed using the model. Subsequently, a novel hot zone identification method was suggested to combine both hot zones from where vulnerable pedestrians originated with hot zones where many pedestrian crashes occur. For the former zones, targeted safety education and awareness campaigns can be provided as countermeasures whereas area-wide engineering treatments and enforcement may be effective safety treatments for the latter ones. Thus, it is expected that practitioners are able to suggest appropriate safety treatments for pedestrian crashes using the method and results from this study.

DEVELOPMENT OF A TRANSIT NETWORK FROM A STREET MAP DATABASE WITH SPATIAL ANALYSIS AND DYNAMIC SEGMENTATION

This paper presents an integrated transit-oriented travel demand modeling procedure within the framework of geographic information systems (GIS). Focusing on transit network development, this paper presents both the procedure and algorithm for automatically generating both link and line data for transit demand modeling from the conventional street network data using spatial analysis and dynamic segmentation. For this purpose, transit stop digitizing, topology and route system building, and the conversion of route and stop data into link and line data sets are performed. Using spatial analysis, such as the functionality to search arcs nearest from a given node, the nearest stops are identified along the associated links of the transit line, while the topological relation between links and line data sets can also be computed using dynamic segmentation. The advantage of this approach is that street map databases represented by a centerline can be directly used along with the existing legacy urban transportation planning systems (UTPS) type travel modeling packages and existing GIS without incurring the additional cost of purchasing a full-blown transportation GIS package. A small test network is adopted to demonstrate the process and the results. The authors anticipate that the procedure set forth in this paper will be useful to many cities and regional transit agencies in their transit demand modeling process within the integrated GIS-based computing environment.

OPTIMAL DETECTOR LOCATION FOR ESTIMATING LINK TRAVEL SPEED IN URBAN ARTERIAL ROADS

Since the estimation and provision of accurate travel speed or time information are essential for proper traffic management and successful ITS deployment, authors tried to identify the best detector locations for relatively long urban links. In most US cities, speed information is generally from loop detectors. In general, however, the data obtained loop detectors in freeways can be a good yardstick for representing the current travel speed and traffic condition. For arterials, however, speed estimation is not as e asy since some variant factors of arterials must also be considered. Factors considered included the following: number of lanes, linklen gth, green time, speed limit, and traffic volumes. With the help of CORSIM traffic simulation, it was found that the optimal detecto r location was mostly related to link length and green time, although the other factors of number of lanes, traffic volumes, and speed limits were not negligible. Conclusively, for relatively long links, approximately 2,000 ft in length, the optimal detector loc ations were identified to be about 200 ft from downstream intersection, for diverse green times of 20, 30, 40 and 50 seconds, respective vely. However, with the increase of link lengths, optimal locations were more dependent on green times. The detailed relationship between optimal detector placement and various link lengths/green times was shown. Some limitations and future research agenda were als o discussed.

Effects of Transportation Accessibility on Residential Property Values: Application of Spatial Hedonic Price Model in Seoul, South Korea, Metropolitan Area

A number of studies have focused on estimating the effects of accessibility on housing values by using the hedonic price model. In the majority of studies, estimation results have revealed that housing values increase as accessibility improves, although the magnitude of estimates has varied across studies. Adequately estimating the relationship between transportation accessibility and housing values is challenging for at least two reasons. First, the monocentric city assumption applied in location theory is no longer valid for many large or growing cities. Second, rather than being randomly distributed in space, housing values are clustered in space—often exhibiting spatial dependence. Recognizing these challenges, a study was undertaken to develop a spatial lag hedonic price model in the Seoul, South Korea, metropolitan region, which includes a measure of local accessibility as well as systemwide accessibility, in addition to other model covariates. Although the accessibility measures can be improved, the modeling results suggest that the spatial interactions of apartment sales prices occur across and within traffic analysis zones, and the sales prices for apartment communities are devalued as accessibility deteriorates. Consistent with findings in other cities, this study revealed that the distance to the central business district is still a significant determinant of sales price.

Numerical simulation of a continuum model for bi-directional pedestrian flow

An algorithm for an extended reactive dynamic user equilibrium model of pedestrian counterflow as a continuum is developed. It is based on a cell-centered high-resolution finite volume scheme with a fast sweeping method for an Eikonal-type equation on an orthogonal grid. A high-order total variation diminishing Runge–Kutta method is adopted for the time integration of semi-discrete equations. The numerical results demonstrate the rationality of the model and efficiency of the algorithm. Some crowd pedestrian flow phenomena, such as dynamic lane formation in bi-directional flow, are observed which are helpful for a global comprehension of pedestrian dynamics. Also, the model can be utilized with different potential applications.

Computation and application of the paired combinatorial logit stochastic user equilibrium problem

The paired combinatorial logit (PCL) model is one of the recent extended logit models adapted to resolve the overlapping problem in the route choice problem, while keeping the analytical tractability of the logit choice probability function. However, the development of efficient algorithms for solving the PCL model under congested and realistic networks is quite challenging, since it has large-dimensional solution variables as well as a complex objective function. In this paper, we examine the computation and application of the PCL stochastic user equilibrium (SUE) problem under congested and realistic networks. Specifically, we develop an improved path-based partial linearization algorithm for solving the PCL SUE problem by incorporating recent advances in line search strategies to enhance the computational efficiency required to determine a suitable stepsize that guarantees convergence. A real network in the city of Winnipeg is applied to examine the computational efficiency of the proposed algorithm and the robustness of various line search strategies. In addition, in order to acquire the practical implications of the PCL SUE model, we investigate the effectiveness of how the PCL model handles the effects of congestion, stochasticity, and similarity in comparison with the multinomial logit stochastic traffic equilibrium problem and the deterministic traffic equilibrium problem.