Statistical and heuristic models have been proposed as applications that are well suited to short-term traffic flow prediction. However, traffic flow data often contain both linear and nonlinear patterns. Therefore, neither statistical nor heuristic models are adequate to model and predict traffic flow data. This paper discusses the relative merits of statistical and heuristic models for traffic flow prediction and summarizes the findings from a comparative study for their performances. Based on that, a hybrid support vector machine for regression (SVR) methodology that combines both statistical and heuristic models is proposed to take advantage of their unique strength in linear and nonlinear modeling. In addition, the dynamics of spatial-temporal patterns in traffic flow are considered in this study, and they are treated as part of the input data. The experiment results based on the real field data of a test region in Beijing suggest that the proposed method is able to provide accurate and reliable flow rate predictions under both low- and high-flow traffic conditions. The benefit from combining statistical and heuristic models as opposed to not combining [autoregressive integrated moving average (ARIMA) model or Elman neural network (NN)] is much more evident in all cases, and the accuracy can be improved by 9.04% on average. Regarding the incorporation of a combination of temporal and spatial characteristics, the use of the hybrid model is found helpful in a one-step-ahead flow rate prediction under high-flow traffic conditions, with a maximum 9.52% improvement on accuracy.

This paper presents the concept of a living laboratory (LL) and how it is applied to transportation operations research through a case study. This case study focuses on calibrating the Wiedemann car-following model parameters specific to freeway work zones. Applying the concept of an LL enables the experimental platform to be in a natural real-world environment. The design of this LL included the development of an instrumented research vehicle (IRV) to capture the natural car-following response of a driver when entering and passing through a freeway work zone. The development of a connected mobile traffic sensing (CMTS) system, which included state-of-the-art intelligent transportation systems (ITS) technologies, supports the LL environment by providing the connectivity, interoperability, and data processing of the natural, real-life setting. The IRV and CMTS system are tools designed based on the research objective to support the concept of an LL which facilitates the experimental environment to capture and calibrate natural driver behavior. This case study shows the application of an LL specific to operations research providing an experimental platform for evaluating the operational performance of a roadway in a real-time, connected, and collaborative natural environment.

Defining road groups is the first step in the Federal Highway Administration (FHWA) factor approach procedure for annual average daily traffic (AADT) estimation and is one of the main sources of errors in AADT estimates. This paper focuses on a comparative analysis of cluster analysis methods to identify road groups with similar traffic patterns according to different combinations of seasonal adjustment factors calculated for passenger vehicles and trucks. The aim is to highlight the differences among methods and input variables in the AADT estimation process, optimizing information commonly available to analysts. The analysis made use of traffic data from 50 automatic traffic recorder (ATR) sites in the Province of Venice, Italy. The estimation accuracy of the clustering methods was assessed and compared by considering the values of mean absolute percent error in AADT estimates. The performance of clustering methods was found to differ, depending on data sets and traffic patterns. Particularly significant for the accuracy of AADT estimates was the choice to use seasonal adjustment factors disaggregated by vehicle type as input variables.

This paper proposes an improved $k$-nearest neighbor ($k$-nn) model for short-term traffic forecasting and examines its applicability to forecasting for different links and time periods. The traditional $k$-nn model is adapted by formulating the weighted distance metric and the state vector, which consider both the temporal and spatial information. The adapted model’s performance is examined in a numerical test where the data are derived from global positioning system (GPS) devices in 180 taxis running in Guiyang, China. The test results demonstrate that the model that considers both the temporal and spatial information outperforms models that only consider temporal information and that adaptation of distance metrics could significantly improve the forecasting accuracy. The adapted model shows the promising performance in comparison with the historical average (HA) model and the artificial neural network (ANN) model. The test results also indicate that information from the upstream and the downstream links plays almost the same important role in predicting the traffic conditions at the target link.

Single-interval forecasting of traffic variables plays a key role in modern intelligent transportation systems (ITSs). Despite the achievements of advanced ITS forecasting in literature, forecast modeling of urban signalized traffic flow, which shows rapid-intensive fluctuations associated with the nonlinear and nonstationary behavior of temporal evolution, is still one of its big challenges. From the perspective of field experts, the mathematical complexity of an advanced model is also a renewal obstacle in practice. On the other hand, the accessibility of large volumes of historical data and the concurrent advanced data management systems used to access them provide data-driven nonparametric regression with a renewal opportunity in practice. In order to address these problems effectively, this paper proposes a $k$ nearest neighbor nonparametric regression (KNN-NPR) forecasting methodology to be tested against vast quantities of real traffic volume data collected from urban signalized arterials. The results show that the KNN-NPR model is clearly superior to two parametric models, Kalman filtering and seasonal autoregressive integrated moving average (ARIMA), in terms of both prediction accuracy and the construction of the directionality of temporal state evolution without a time-delayed response. Consequently, it appears that KNN-NPR, even though it is very simplified, is able to efficaciously capture the complex behavior of urban signalized traffic flow.

This paper presents a new formulation for the network design problem as it relates to retrofitting existing roadway infrastructure for bicycles. The goal of the problem is, for a minimum cost, to connect all origin–destination pairs with paths where each roadway segment and intersection meets or exceeds a lower bound on its bicycling level of service. The length of each optimal path is constrained to be no greater than a given upper bound, which is expressed as a function of shortest path length. Experimental analysis on the Austin, Texas downtown region shows that a systems approach will yield different results than an approach that separately considers connecting each pair of origins and destinations, and that placing an upper bound on the amount of deviation from the shortest path will impact the design decisions. Model parameters, although the defaults are based on existing research, should be calibrated based on local data. Variants on the formulation are provided that allow for a trade-off between optimality and computational efficiency.

Single-lane modern roundabouts are one of the most important intersection types in the suburbs of Australia. Therefore, it is important to estimate their entry capacities. In this case study, an analytical model based on the gap acceptance theory by incorporating the effects of the exiting vehicles is proposed. Then, a scenario analysis is carried out to assess the effects of the exiting indicators. This is followed by the discussion of the applicability of the proposed model. The results show that the transport authorities need to strictly enforce the use of indicators before exiting in order to achieve higher capacity.

The purpose of this study is to assess the adherence of buses to their schedules when they are operating in specific conditions made by separating buses from fast general traffic by median islands, but still sharing curbside lanes with slow traffic (e.g., cars and motorcycles) on an urban arterial road with heavy traffic in Taiwan. This study uses real-time traffic data (e.g., travel time, speed, and traffic volume) collected for a 3-month period in 2010 from buses equipped with a global positioning system (GPS) and inductive loop vehicle detectors buried under the road. The ordered probit and multinomial logit models are applied to examine variables contributing to trip time between two adjacent stops being later than the scheduled trip time during different time periods of the day. The findings indicate that variables significantly influence variations in adherence to schedule, including buses operating in mixed-traffic curbside lanes, the number of bus routes passing through a stop, bus dwell time at a stop, bus headway, traffic volume, the number of intersections without traffic signals, spacing of bus stops with fewer than 300 m between them in densely populated areas, and rainy weather.