Evaluating the impact of OOCEA's dynamic message signs (DMS) on travelers' experience using multinomial and ordered logit for the Post-Deployment Survey

The purpose of this thesis was to evaluate the impact of dynamic message signs (DMS) on the Orlando-Orange County Expressway Authority (OOCEA) toll road network using the Post-Deployment DMS Survey analysis. DMS are electronic traffic signs used on roadways to give travelers information about travel times, traffic congestion, accidents, disabled vehicles, AMBER alerts, and special events. The particular DMS referred to in this study are large rectangular signs installed over the travel lanes and these are not the portable trailer mount signs. The OOCEA has added twenty-nine fixed DMS to their toll road network from 2006-2008. At the time of the post-deployment survey, a total of twenty-nine DMS were up and running on the OOCEA toll road network. Since most of the travelers on the OOCEA toll roads were from Orange, Osceola, and Seminole counties, this study was limited to these counties. This thesis documents the results for the post-deployment survey analysis. The instrument used to analyze the travelers' perception of DMS was a survey that utilized computer aided telephone interview. The post-deployment survey was conducted during the month of May, 2008. Questions pertaining to the acknowledgement of DMS on the OOCEA toll roads, satisfaction with travel information provided on the network, formatting of the messages, satisfaction with different types of messages, diversion questions (Revealed and Stated preferences), and classification/socioeconomic questions (such as age, education, most traveled toll road, county of residence, and length of residency) were asked to the respondents. This thesis is using results of the multinomial logit model for diversion of traffic. This model takes into account the different diversion decisions from the post development survey (stay vs. divert all the way vs. divert and come back vs. abandon trip) and explains the differences in the diversion behavior. Drivers that use SunPass or Epass tend to stay on the toll road during unexpected congestion. Frequent SR 408 users are more likely to divert and stay off the toll road and frequent SR 417 users are more likely to divert and get back on the toll road. Drivers whose stated preference was to divert off the toll road were more likely to do the same in the real world. However, not too many of the respondents were likely to abandon their trips in the real world even if they said they would in a hypothetical congestion scenario. Users of 511 were more likely to divert and get back on the toll road or abandon their trips due to unexpected congestion. OOCEA can use this study to concentrate on keeping their toll roads more attractive during unexpected congestion to keep drivers from diverting all the way or abandoning their trips. For example, better incident management in clearing accidents more efficiently (thereby decreasing delay) and encouraging the use of SunPass or EPass could help drivers stay than divert or abandon their trip. This thesis also used ordered logit model for satisfaction. This model explains the levels of magnitude of satisfaction with traveler information on OOCEA toll roads. Drivers who acquired traveler information from DMS were less likely to be dissatisfied with traveler information provided on toll roads than other respondents. Drivers who were satisfied with accuracy and information on hazard warnings on DMS were more likely to be satisfied with information provided on toll roads than other respondents. This thesis provides a microscopic insight on the driver behavior on toll roads. This thesis expands the diversion and satisfaction models from previous studies in a way that OOCEA can identify specific groups of drivers related to a given response behavior (i.e., diverts off toll roads or dissatisfied with traveler information). Such analysis can be conducted in the future in the same study area or replicated in other areas to quantify the effects of individual and choice related attributes on choice behavior

Modeling Driver Behavior in Work and Nonwork Zones

A new multidimensional framework for modeling car following on the basis of statistical evaluation of driver behavior in work and nonwork zones is presented. The models developed as part of this multidimensional framework use psychophysical concepts for car following that are close in character to the Wiedemann model used in popular traffic simulation software such as VISSIM. The authors hypothesized that with an instrumented research vehicle (IRV) in a living laboratory (LL) along a roadway, the parameters of models developed from the multidimensional framework could be derived statistically and calibrated for driver behavior in work zones. This hypothesis was validated with data collected from a group of 64 random participants who drove the IRV through an LL set up along a work zone on I-95 near Washington, D.C. For this validation, the IRV was equipped with sensors, including radar, and an onboard data collection system to record the vehicle performance. One of the limitations of current car-following models is that they account for only one overall behavioral condition. This study demonstrated that there are four different categories of car-following behavior models, each with different parameter distributions: the four categories are divided by traffic condition (congested versus noncongested) and by roadway condition (work versus nonwork zone). Calibrated threshold values for each of these four categories are presented. Furthermore, this new framework for modeling car-following behavior is described in a multidimensional setting and can be used to enhance vehicle behavior in microsimulation models