STR: Seasonal-Trend Decomposition Using Regression

We propose a new method for decomposing seasonal data: a seasonal-trend decomposition using regression (STR). Unlike other decomposition methods, STR allows for multiple seasonal and cyclic components, covariates, seasonal patterns that may have noninteger periods, and seasonality with complex topology. It can be used for time series with any regular time index, including hourly, daily, weekly, monthly, or quarterly data. It is competitive with existing methods when they exist and tackles many more decomposition problems than other methods allow. STR is based on a regularized optimization and so is somewhat related to ridge regression. Because it is based on a statistical model, we can easily compute confidence intervals for components, something that is not possible with most existing decomposition methods (such as seasonal-trend decomposition using Loess, X-12-ARIMA, SEATS-TRAMO, etc.). Our model is implemented in the R package stR, so it can be applied by anyone to their own data.

The Quick and the Careless: The Construct Validity of Page Time as a Measure of Insufficient Effort Responding to Surveys

Several recent studies have examined the prevention, causes, and consequences of insufficient effort responding (IER) to surveys. Scientific progress in this area, however, rests on the availability of construct-valid IER measures. In the current paper we describe the potential merits of the page time index, which is computed by counting the number of questionnaire pages to which a participant has responded more quickly than two seconds per item (see Huang et al., 2012 ). We conducted three studies (total N = 1,056) to examine the page time index's construct validity. Across these studies, we found that page time converged highly with other IER indices, that it was sensitive to an experimental manipulation warning participants to respond carefully, and that it predicted the extent to which participants were unable to recognize item content. We also found that page time's validity was superior to that of total completion time and that the two-seconds-per-item rule yielded a construct-valid page time score for items of various word lengths. Given its apparent validity, we provide practical recommendations for the use of the page time index.

A Model-based Parameter Optimization Control Strategy for Trajectory Tracking with Torque and Velocity Constraints

The wheeled robots trajectory tracking control methods rarely constrain the torque and speed at the same time. In actual application, the torque and speed of the robot cannot exceed the saturation limit of the actuator. This paper develops a model-based trajectory tracking parameter optimization controller with both velocity and torque constraints, using a gradient descent parameter iterative learning strategy to minimize the settling time index of the system. Trajectory tracking time optimization methods usually require a given analytical expression of the system time, while this time optimization method only requires that the settling time is solvable. The MATLAB simulation experiments show that the proposed parameter optimization controller for trajectory tracking can perform velocity and torque constraints while having a relatively good overall rapidity time index. If the resolution of the robot sensor can meet the design requirements, the optimization method can strictly control the system torque maximum to a reasonably small expected value. When the resolution of the robot sensor is limited, this optimization method can restrict the system torque maximum within a reasonable saturation constraint range.

Empirical Analysis for Measuring Travel Time Reliability on Road Network

This research analyses the characteristics of travel time reliability for the road network in Kota Bandar Lampung. Therefore, travel time consists of access, wait and interchange time, while its reliability deals with variations of in-passenger/private cars time. Survey of travel time on each road was carried out for 12 hours (from 06.00 to 18.00) for five working days. Furthermore, the buffer time method was used to measure the characteristics of time travel reliability consisting of five measuring tools, namely planning time, planning time index, buffer time, buffer time index and travel time index. This research found that the temporal effects are the main factor that tends to affect travel time, whereas network effects are the second factor that tends to affect travel time. Furthermore, the regression equation was developed to express the effect of planning time (TPlan) and free-flow travel time on average travel time .

TRAVEL TIME RELIABILITY INDICES FOR URBAN ROUTES IN BAGHDAD CITY

Reliability is one of the main metrics of transport system efficiency and quality of service. For both travelers and transport management organizations, the high variance of road travel times has become a problem. Reliability has been identified as one of the main areas of interest of the Strategic Highway Research Plan II. In order to evaluate congestion and unexpected changes in travel time, reliability metrics are increasingly used. GPS devices provide for exact assessment of travel time for each connection along the routes used for this research. (14 Ramadan arterial street, Al-Karada arterial street and Damascus arterial street). A GPS-equipped instrumented car was used to gather 50 test runs at peak and off peak times. At peak and off peak hours, 50 test runs were obtained using a GPS-equipped instrumented car. Raising the buffer time index results in inferior conditions for reliability. A buffer index of AL- Karada street was created about 53% and 30% for Damascus street and finally for 14 Ramadan street which present a 29% buffer index for north direction. As for its southern direction 14 Ramadan street created a buffer index of about 65% and 33% for AL- Karada street and finally for Damascus street which present a 29% buffer index. In addition, travel time index for (14 Ramadan street, AL- Karada street and Damascus street) respectively is about 2.8 %, 3.3% and 2.6% for north direction, as for its southern direction the travel time index is obtained for (14 Ramadan street, AL- Karada street and Damascus street) respectively were a 3%,3.7%, and 2.5%. Finally, the 95% percentile travel time for observed three selected routes in this study, the extra delay was felt on each route (1627, 2212, and 1192) sec. for (14 Ramadan street, AL- Karada street and Damascus street) for north direction, as for its southern direction the extra delay that perceived on each route (2221, 2132, and 975) sec. for (14 Ramadan street, AL- Karada street and Damascus street) respectively.

Perceived Trip Time Reliability and Its Cost in a Rail Transit Network

Time reliability in a Rail Transit Network (RTN) is usually measured according to clock-based trip time, while the travel conditions such as travel comfort and convenience cannot be reflected by clock-based trip time. Here, the crowding level of trains, seat availability, and transfer times are considered to compute passengers’ Perceived Trip Time (PTT). Compared with the average PTT, the extra PTT needed for arriving reliably, which equals the 95th percentile PTT minus the average PTT, is converted into the monetary cost for estimating Perceived Time Reliability Cost (PTRC). The ratio of extra PTT needed for arriving reliably to the average PTT referring to the buffer time index is proposed to measure Perceived Time Reliability (PTR). To overcome the difficulty of obtaining passengers’ PTT who travel among rail transit modes, a Monte Carlo simulation is applied to generated passengers’ PTT for computing PTR and PTRC. A case study of Chengdu’s RTN shows that the proposed metrics and method measure the PTR and PTRC in an RTN effectively. PTTR, PTRC, and influential factors have significant linear relations among them, and the obtained linear regression models among them can guide passengers to travel reliably.

Researching Relationships between Truck Travel Time Performance Measures and On-Network and Off-Network Characteristics

Trucks serve significant amount of freight tonnage and are more susceptible to complex interactions with other vehicles in a traffic stream. While traffic congestion continues to be a significant ‘highway’ problem, delays in truck travel result in loss of revenue to the trucking companies. There is a significant research on the traffic congestion mitigation, but a very few studies focused on data exclusive to trucks. This research is aimed at a regional-level analysis of truck travel time data to identify roads for improving mobility and reducing congestion for truck traffic. The objectives of the research are to compute and evaluate the truck travel time performance measures (by time of the day and day of the week) and use selected truck travel time performance measures to examine their correlation with on-network and off-network characteristics. Truck travel time data for the year 2019 were obtained and processed at the link level for Mecklenburg County, Wake County, and Buncombe County, NC. Various truck travel time performance measures were computed by time of the day and day of the week. Pearson correlation coefficient analysis was performed to select the average travel time (ATT), planning time index (PTI), travel time index (TTI), and buffer time index (BTI) for further analysis. On-network characteristics such as the speed limit, reference speed, annual average daily traffic (AADT), and the number of through lanes were extracted for each link. Similarly, off-network characteristics such as land use and demographic data in the near vicinity of each selected link were captured using 0.25 miles and 0.50 miles as buffer widths. The relationships between the selected truck travel time performance measures and on-network and off-network characteristics were then analyzed using Pearson correlation coefficient analysis. The results indicate that urban areas, high-volume roads, and principal arterial roads are positively correlated with the truck travel time performance measures. Further, the presence of agricultural, light commercial, heavy commercial, light industrial, single-family residential, multi-family residential, office, transportation, and medical land uses increase the truck travel time performance measures (decrease the operational performance). The methodological approach and findings can be used in identifying potential areas to serve as truck priority zones and for planning decentralized delivery locations.