No abstract

No abstract

This study evaluated the safety performance of two types of climbing lane configurations—pocket and overtaking—that are implemented on South Korean expressways. For the evaluation, empirical Bayes (EB) and turning point analyses were sequentially applied to compare crash occurrences before and after the configuration change from pocket to overtaking types on 30 climbing lanes. First, the EB analysis with a meta-analysis indicated that the overall safety performance was not significantly degraded after the configuration conversion. However, because some sites were associated with substantial increases in crash occurrences, 11 sites were identified with crashes above the upper bound of a confidence interval estimated from the meta-analysis. A turning point analysis was applied to those sites to validate whether the increase in crash occurrences and the configuration conversions were consistent in the temporal trend. The results show that changes in crash occurrence at three study sites did not coincide with the configuration change, suggesting no evidence for causality. This implies that in a comparative analysis, evaluation of time series of crash occurrences is important, in addition to a comparison of crash frequencies before and after the implementation of roadway facilities. This study suggests that the developed method can be used to evaluate the safety performance of implementing a roadway facility and for selecting sites where the facility degraded the safety performance and required further evaluation by field engineers.

Real-world route navigation data indicate that nontrivial portion of drivers do not prefer the system-recommended best routes. Current navigation systems have simplified assumptions about drivers' route choice preferences and do not adequately accommodate drivers' heterogeneous route choice preferences, mainly because of: (i) difficulty in acquiring exogenous criteria (e.g., sociodemographic information) that are typically used to differentiate drivers' preferences in behavioral modeling; and (ii) difficulty in capturing preference of individuals due to limited preference data at the individual level. To address these, this paper introduced a human-centric machine learning technique named Multi-Task Linear Classification Model Adaption (MT-LinAdapt). It can capture drivers' common aspects of route choice preferences and yet adapts to each driver's own preference. In addition, any evolvement of individual drivers' preferences can be simultaneously integrated to update the common preference for further individual drivers' preference adaptation. This paper evaluated MT-LinAdapt against two state-of-the-art route recommendation strategies including an aggregate-level and an individual-level data-based strategies, which are categorized based on the data used for modeling. With a real-world dataset containing 30,837 drivers' navigation usage data in Daegu City, South Korea, MT-LinAdapt was compared to existing strategies for its performance at different levels of data availability, and showed at least the same performance with existing strategies when minimum preference data is available and achieves up to 7% higher prediction accuracy as more data becomes available. Higher prediction accuracies are expected to bring better user satisfaction and compliance rates which can further help with transportation system control and management strategies.

Road links within a city are hierarchical according to their structure and function. Upper-level road links, such as highways and arterials, are designed to maintain higher mobility and traffic flow, while lower-level road links should be more accessible. However, depending on the origin-destination demand pattern (O-D), drivers' route choice, land use, and urban infrastructure, the actual usage pattern of roads could be different from the designed intention. This difference ultimately puts a load on certain road links and causes traffic jams. In order to handle this issue, it is necessary to create an appropriate evaluation method for the functionality of road links in advance. The research suggests an evaluation method to examine the functionalities of the roadways by using real-world mobility data and weighted network analysis. In the study, the roles of links were defined and quantified by three network attributes, in-strength, out-strength, and betweenness centrality. Derived attributes were used to cluster links with similar travel patterns. Furthermore, the concept of link reliability was introduced to measure the reliance of the network on individual links. Those network indices make it possible to evaluate the functioning of roads based on people's travel patterns and to detect critical links that are irreplaceable and difficult to detour. This information can be used to determine the priorities of upcoming improvements and ultimately improve the efficiency of operation and maintenance of the road link networks.