Complex movement patterns of pedestrian traffic, ranging from unidirectional to multidirectional flows, are frequently observed in major public infrastructure such as transport hubs. These multidirectional movements can result in increased number of conflicts, thereby influencing the mobility and safety of pedestrian facilities. Therefore, empirical data collection on pedestrians’ complex movement has been on the rise in the past two decades. Although there are several reviews of mathematical simulation models for pedestrian traffic in the existing literature, a detailed review examining the challenges and opportunities on empirical studies on the pedestrians complex movements is limited in the literature. The overall aim of this study is to present a systematic review on the empirical data collection for uni- and multidirectional crowd complex movements. We first categorized the complex movements of pedestrian crowd into two general categories, namely, external governed movements and internal driven movements based on the interactions with the infrastructure and among pedestrians, respectively. Further, considering the hierarchy of movement complexity, we decomposed the externally governed movements of pedestrian traffic into several unique movement patterns including straight line, turning, egress and ingress, opposing, weaving, merging, diverging, and random flows. Analysis of the literature showed that empirical data were highly rich in straight line and egress flow while medium rich in turning, merging, weaving, and opposing flows, but poor in ingress, diverging, and random flows. We put emphasis on the need for the future global collaborative efforts on data sharing for the complex crowd movements.

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Expansion factors based on the trends in long-term count data are useful tools for estimating daily, weekly, or annual volumes from short-term counts, but it is unclear how to differentiate locations by activity pattern. This paper compares two approaches to developing factor groups for hour-to-week pedestrian count expansion factors. The land use (LU) classification approach assumes that surrounding LUs affect the pedestrian activity at a location, and it is easy to apply to short-term count locations based on identifiable attributes of the site. The empirical clustering (EC) approach uses statistical methods to match locations based on the actual counts, which may produce more accurate volume estimates, but presents a challenge for determining which factor group to apply to a location. We found that both the LU and EC approaches provided better weekly pedestrian volume estimates than the single factor approach of taking the average of all locations. Further, the differences between LU and EC estimation errors were modest, so it may be beneficial to use the intuitive and practical LU approach. LU groupings can also be modified with insights from the EC results, thus improving estimates while maintaining the ease of application. Ideal times for short-term counts are during peak activity periods, as they generally produce estimates with fewer errors than off-peak periods. Weekly volume estimated from longer-duration counts (e.g., 12 h) is generally more accurate than estimates from shorter-duration counts (e.g., 2 h). Practitioners can follow this guidance to improve the quality of weekly pedestrian volume estimates.

Intersections are hazardous places. Threats arise from interactions among pedestrians, bicycles and vehicles, more complicated vehicle trajectories in the absence of lane markings, phases that prevent determining who has the right of way, invisible vehicle approaches, vehicle obstructions, and illegal movements. These challenges are not fully addressed by the "road diet" and road redesign prescribed in Vision Zero plans, nor will they be completely overcome by autonomous vehicles with their many sensors and tireless attention to surroundings. Accidents can also occur because drivers, cyclists and pedestrians do not have the information they need to avoid wrong decisions. In these cases, the missing information can be computed and broadcast by an intelligent intersection. The information gives the current full signal phase, an estimate of the time when the phase will change, and the occupancy of the blind spots of the driver or autonomous vehicle. The paper develops a design of the intelligent intersection, motivated by the analysis of an accident at an intersection in Tempe, AZ, between an automated Uber Volvo and a manual Honda CRV and culminates in a proposal for an intelligent intersection infrastructure. The intelligent intersection also serves as a software-enabled version of the 'protected intersection' design to improve the passage of cyclists and pedestrians through an intersection.