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54 Conclusions and Discussion Because participants could receive monetary compensation or free use of leased vehicles, they were, to some extent, self- The proposed multistate model provides a better fit to field data selected. Data reduction revealed that a relatively large portion compared with traditional unimodal travel time models. The of the subjects were students, hourly workers, or from other distribution of field travel time is tested to be multimode. As low-income populations. Consequently, relatively fewer home- demonstrated in the last row of Table 6.1, the reliability mea- to-work or work-to-home trips were collected, resulting in a sures generated from the proposed model are specific and mea- limited size of trips at regular peak hours collected. surable.Specifically,thetwomodel parameters--the probability Additionally, there are other limitations. For example, the of encountering congestion and the probability of an expected instrumented cars were sometimes driven by other drivers travel time--are both specific and measurable. The proposed instead of the participant who signed up for the data collection. travel time reliability reporting is achievable because the model Consequently, the trips collected by GPS reflect multiple driv- can be developed using in-vehicle, loop detector, video, or other ers' travel patterns. Instead of making regular trips sharing surveillance technologies. Running this model is not time-con- starting and ending points, some data sets illustrated a rather suming, so it can provide timely information. Consequently, complicated travel pattern in which the trips recorded are rel- the proposed model provides valuable information to assist atively scattered, covering an expanded road network. travelers in their decision-making process and facilitates the Another limitation is that, in the 100-Car Study, the management of transportation systems. computer time was used instead of a synchronized time, Travel time reliability can be enhanced by modifying driver behavior to reduce incidents. The proposed model is designed which resulted in some errors in time stamp. Consequently, to model travel time reliability and congestion before and after even though the team does have a high-quality travel time incident-induced congestion. The events have been viewed by database collected and maintained by the state of Virginia, data reductionists and designated as correctable or preventa- it is hard to link the in-vehicle data with such external travel ble by modifying driver behavior. Ideally, the data will also time data. incorporate a data set with sufficient peak hour travel time The statistical model proposed in this chapter used other data with and without the influence of safety-related events. It travel time data rather than candidate data sources because of is relatively easy to capture correctable driver behavior with some limitations of those data. If future data collection is care- the aid of the data reduction tool developed by VTTI. The fully designed with the recommendations the team proposes challenge is to collect travel time data before and after these (as discussed in Chapter 8 of this report), the data will be sig- events. The original plan was to use the in-vehicle naturalistic nificantly improved to serve this research goal. data collected in the candidate studies by VTTI and related external data using in-vehicle time and location information, References but using such data is much more complicated and infeasible 1. Chalumuri, R. S., T. 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