Estimation Method for Interurban Accessibility: A Highway Performance Measure Indicating Smooth and Safe Traffic Flow

Abstract

Developing driver-oriented highway performance measures is the key to performance-based highway design. Highways should be designed so that an appropriate performance measure corresponding to major highway functions can achieve a required level of quality of traffic service. Quantitative assessments of performance measures need to evaluate the achieved level of performance as compared with the required level. Various measures have been proposed, such as overall travel speed, target travel speed, travel time, traffic safety, traffic density, congestion, freedom to maneuver, and driving comfort and convenience (HRB, 1965; TRB, 1985; FGSV, 1988). However, it is unclear how existing performance measures relate to driver perceptions of the quality of traffic service. This study focuses on ease of access to destinations as a new driver-oriented performance measure under various driving conditions on interurban expressways connecting hub cities. This measure indicates the ease of maintaining vehicle speed near a target travel speed while maintaining a constant headway in the lane. We hereinafter refer to this measure as “interurban accessibility.” Interurban accessibility can be expressed as the sum total of utility that each driver perceives from smooth and safe traffic flow at each point in interurban expressway lanes. The objectives of this study are twofold. One objective is to develop an estimation method for interurban accessibility based on driver utility as proposed by Kita et al. (2014) . Their experimental results indicate that our proposed measure more appropriately describes driver perceptions of the quality of traffic service than do previously proposed performance measures. The other objective is to demonstrate how to use interurban accessibility to determine the planning level of the performance measure in comparison with a required level of quality of traffic service. In modeling the interurban accessibility measure, the level of interurban accessibility decreases as travel time between hub cities increases or as freedom to maneuver decreases because of congestion and delays. Hence, our model of interurban accessibility assumes a target travel time, defined as the travel distance divided by the target travel speed, so interurban accessibility is presumed to increase with travel speed. In addition, Kita and Kouchi (2011) showed that perception structures of interurban accessibility are momentarily formed by accumulating perceived values. Hence, our model involves both average and minimum point-based utilities in microscopic driving environments, so that accident risks contingent on speed can be considered for each point-based utility. Moreover, accumulation of point-based utilities between hub cities is considered to exert a decisive influence on the level of interurban accessibility. Driver memory is assumed to decay over elapsed driving time. Hence, accumulated point-based utilities are multiplied by a time discount factor, so that average point-based utilities are obtained to average the discounted point-based utilities. This study proposes a method for assessing the planning level of performance measures by means of an interurban accessibility estimation model, as compared with a required level of quality of traffic service. We therefore utilize exceedance probability of a probability distribution function regarding interurban accessibility. Note that, regarding traffic management and control through the use of a highway performance measure, it is believed to be better to apply data from roadside continuous-traffic-monitoring devices. Therefore, this study proposes a method for assessing interurban accessibility by examining traffic volume, which is easy to observe at each device. Before assessing the planning level of the measure as compared with the required level, the distribution of the interurban accessibility measure must be particularized. We presents a flowchart for specifying the distribution of a measure. The procedure of this flowchart requires a probability distribution of instant utility and a probability distribution of difference in instant utility between two points. The former requires distributions of time headway and spot speed, and the latter requires microscopic driving environments such as space headway that each driver perceives. Both the former and the latter require data on traffic volume and travel distance. The travel distance here means a given highway distance between hub cities. The distributions of the time headway and spot speed are affected by traffic characteristics such as road gradient and large-vehicle mix rate, in addition to the traffic volume. The following describes the procedure of the proposed flowchart. Firstly, assuming that the distribution of the time headway and that of the spot speed can be approximated by a log-normal distribution and a normal distribution, respectively, data on both the time headway and the spot speed are generated. Secondly, assuming that the distribution of the instant utility and that of the difference in instant utility between two neighboring points can be approximated by a shifted log-normal distribution and a Cauchy distribution, respectively, data on both the instant utility and the difference in instant utility between two points are generated. Thirdly, according to the point-based utility model, the number of point-based utilities for each point is generated based on Monte Carlo simulation employing both the distribution of the instant utility and that of the difference in instant utility between two points. Finally, a distribution of the interurban accessibility can be obtained from the interurban accessibility estimation model. Here, as a critical value indicating an exceedance probability of 85% in this distribution, a planning level of the performance measure is considered. Through a numerical example, we confirmed that the proposed measure can better describe driver perception than can existing measures such as travel speed. Consequently, we believe that performance-based highway design considering data obtained from vehicle detectors is possible by considering interurban accessibility as the performance measure.

Keywords

Performance-based highway design ; Highway performance measure ; Accessibility indicator ; Utility-based approach ; Planning level ; Achievement level ; Interurban accessibility

References

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