Near future travel-time information is one of the most critical factors that travellers consider before making trip decisions. In efforts to provide more reliable future travel-time estimations, transportation engineers have examined various techniques developed in the last three decades. However, there have not been sufficiently systematic and through reviews so far. In order to effectively support various transportation strategies and applications including Intelligent Transportation Systems (ITS), it is necessary to apply appropriate forecasting methods for matching circumstances in a timely manner. This paper conducts a comprehensive review study focusing on literatures, including modern techniques proposed recently, related to travel time and traffic condition predictions that are based on ‘data-driven' approaches. Based on the underlying mechanisms and theoretical principles, different approaches are categorized as parametric (linear regression and time series) and non-parametric approaches (artificial intelligence and pattern searching). Then, the approaches are analysed for their strengths, potential weaknesses, and performances from five main perspectives that are prediction range, accuracy, efficiency, applicability, and robustness.

Existing simulation models have difficulty in accurately modeling oversaturated traffic conditions on freeways. A new behavioral algorithm for oversaturated freeway flow can be used in microscopic simulation models; it was developed as part of the Next Generation Simulation (NGSIM) project, sponsored by FHWA. The proposed algorithm is an integrated car-following and lane-changing modeling framework that is consistent with the kinematic wave theory. The algorithm can explicitly model mandatory and discretionary lane changing, including cooperation during lane changing. As an extension of the algorithm, a new on-ramp merging model was developed and incorporated into the model. The proposed algorithm also accounts for the relaxation process following lane changing. The proposed model includes a small number of parameters that can be readily measured in the field. The proposed model was implemented into a microscopic simulator, and it was validated with both vehicle trajectory data and macroscopic detector data from an NGSIM test site. The results show good agreement with real-world traffic behavior. The study products, including an algorithm description, analysis results, and computer code, are documented and made available to developers and users of traffic simulation tools.

Cost-effective maintenance of infrastructure systems within an acceptable level of safety and performance is the major concern of managing agencies. Recent maintenance approaches have offered two distinct maintenance policies: time-based maintenance (TBM) and condition-based maintenance (CBM). This paper compares the two policies under different cost environments for stochastically deteriorating infrastructures. The performance of TBM and CBM is evaluated from the viewpoint of condition transition and life cycle cost. We found the optimal maintenance solutions for TBM and CBM using dynamic programming and performed a simulation study. The simulation study showed that TBM causes some unexpected deterioration that leads to high cost, while CBM maintains a certain level of condition steadily under consistent inspection, which enables steady spending at the management level. The life cycle cost under CBM is relatively symmetric and has a more concentrated distribution than TBM, which has a large number of outliers from unexpected deteriorations. Finally, we evaluated the life cycle cost with a change in the inspection–repair cost ratio to find the most appropriate cost environment for each maintenance policy. While CBM needs periodic inspections, it still has more advantages than TBM when the inspection cost is relatively low.

With recent aging demographic trends, the needs for enhancing geo-spatial analysis capabilities and monitoring the status of accessibilities of its citizens with healthcare services have increased. The accessibility to healthcare is determined not only by geographic distances to service locations, but also includes travel time, available modes of transportation, and departure time. Having access to the latest and accurate information regarding the healthcare accessibility allows the municipal government to plan for improvements, including expansion of healthcare infrastructure, effective labor distribution, alternative healthcare options for the regions with low accessibilities, and redesigning the public transportation routes and schedules. This paper proposes a new method named, Seoul Enhanced 2-Step Floating Catchment Area (SE2SFCA), which is customized for the city of Seoul, where population density is higher and the average distance between healthcare-service locations tends to be shorter than the typical North American or European cities. The proposed method of SE2SFCA is found to be realistic and effective in determining the weak accessibility regions. It resolves the over-estimation issues of the past, arising from the assignment of high healthcare accessibility for the regions with large hospitals and high density of population and hospitals.

Having access to the future traffic state information is crucial in maintaining successful intelligent transportation systems (ITS). However, predicting the future traffic state is a challenging research subject involving prediction reliability issues. Predictive performance measures, including the accuracy, efficiency, and stability, are generally considered as the most important priorities in the evaluation of prediction modules. Researchers have developed various K-nearest-neighbors-based searching algorithms that find the future state from the historical traffic patterns. Interestingly, there has not been sufficient effort made for improving the performance. For the emerging big data era, incorporating an efficient search strategy has become increasingly important since the applicability of the prediction module in ITS heavily relies on the efficiency of the searching method used. This paper develops a novel sequential search strategy for traffic state predictions. The proposed sequential strategy is found to be outperforming the conventional single-level search approach in terms of prediction measures, which are prediction accuracy, efficiency, and stability. Compared with the conventional approach, the proposed sequential method yields significantly more accurate results via internal hierarchical improvements across sublevels while maintaining excellent efficiency and stability.

Capacity drop, which is defined as discharge flow drop after bottleneck activation, has been frequently observed on urban highways, especially in merging sections. Maintaining high capacity on roadways is a main concern for traffic operators, theorists, and transportation modelers. Accordingly, many researchers have investigated capacity drop, yet highway capacity and discharge flow measurement methods vary, and results are not comparable. A systematic methodology is introduced for finding capacity drops by using detector data to estimate roadway capacity and discharge flow. The impact of the number of lanes on capacity drops at merging sections on highways is investigated. Results show that capacity drop is negatively related to the number of lanes. Detailed information is analyzed for individual lanes and off-ramp effect on capacity drop. Individual lane analysis supports the negative relationship between the amount of capacity drop and the number of lanes. A decrease in capacity drop is observed when the flow ratio of the off-ramp increases.