![]() Accurate knowledge (or inaccurate knowledge) of the peak hour can have far reaching implications when it comes to traffic management. The Highway Capacity Manual makes use of PHF in roughly 100 cases, including Levels of Service (LOS) and signal timing calculations. The peak hour (and by extension, the PHF) is used as an input in many traffic related calculations and workflows. Why are peak hour measurements important? What is the probability of a peak hour measurement being correct given that the data used to compute it is only accurate to a certain level? In addition, does this probability depend on the type of road (total volume, original Peak Hour Factor ) that is being measured? To start, we focused specifically on the calculation of peak hour, seeking to answer the formalized question: To better understand the effects of inaccurate data, UrbanLogiq applied a statistical simulation to analyze the different levels of accuracy of collected traffic volume data and its effects. Accordingly, questions often arise about the accuracy of different data sources. Emerging and traditional datasets, such as manual counts and data from traffic sensors are increasingly used together in transportation engineering, operations, and planning workflows. Internet of Things (IoT) sensory technology, connected vehicles, location-based data, telecommunications data, computer vision… the list goes on. The following implementation steps are suggested: (a) high-priority employment locations (b) obtain support for feasibility studies (c) conduct work schedule and transportation surveys of employers design work rescheduling plans (e) obtain management decisions to implement (f) provide implementation assistance (g) evaluate impacts and (h) refine and extend the program.The last few years have seen an explosion of new datasets to measure traffic patterns. After commitments are obtained from public and private organizations, a lead agency should be established, preferably the same one that is coordinating ridesharing. Implementation of an alternative work schedule program begins with the determination that there is a congestion problem that could be alleviated by shifting transportation demand to less congested periods. Theoretical analyses indicate that compressed workweeks can significantly reduce peak-period work trips and congestion although there may be negative effects on carpooling and transit ridership. It appears that flexible hours programs have a positive effect on transit and carpool use. Staggered and flexible work hours result in reduced travel times, reduced load factors, and thus less crowding on transit and less waiting time for elevators in buildings. ![]() Evaluations of large- scale variable work hours programs show that peak-hour bus loads and automobile arrivals at parking garages decrease 10 to 20 percent, and peak-hour automobile traffic volumes on major approaches to work centers are reduced by 5 to 10 percent. Alternative work schedules can be used to manage transportation demand by shifting commuters away from the peak hours and by reducing the number of days that people need to travel to work. This synthesis presents information on implementation of staggered and flexible work hours and compressed workweeks and on the impacts of such measures on highways, transit systems, and ride-sharing programs.
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