Behavior Insights for an Incentive-Based Active Demand Management Platform

Most current Travel Demand Management(TDM)programs such as vanpooling,ridesharing,or transit focus on managing travel demand of specific groups of commuters but are limited in effectively managing demand for automobile drivers,who are unable or unwilling to participate in such programs.This paper highlights results from a pilot field study conducted in a large west coast city experiencing major traffic congestion,and documents results of the use of an incentive-based active demand management(ADM)system focusing on automobile commuters.The system,called“Metropia,”predicts future traffic conditions,applies a proprietary routing algorithm to find time-dependent shortest paths for different departure times,and,based on user request,provides automobile travelers with multiple departure times and route choices.Each of these travel choices are assigned points values,with higher points(and thus more valuable rewards)available for travelling during off-peak times and less congested routes,and lower points available for peak traffic travel times.The goal of this ADM system is to improve traffic flow and commuter travel times citywide,alleviating heavily congested areas without the use of new roadway construction by incentivizing travelers to change their travel behavior and avoid traffic congestion.The level of rewards points available to users(commuters)by the system depends on the travelers’behavioral change degree and their contributions to traffic congestion alleviation.This system was implemented in Los Angeles,Calif.,USA,as a small scale pilot field study carried out beginning April 2013 and lasting for 10 weeks.Results from this field study show the system is able to accurately predict travel time with Relative Mean Absolute Error(RMAE)as low as 15.20%.Significant travel behavior changes were observed which validate the concept of using incentives to influence people’s travel behavior.Furthermore,field study results show 20%travel time can be saved for people who changed their travel behavior.

Defensive pressure affects basketball technical actions but not the time-motion variables

Background: Novel player tracking technologies can change the understanding of performance determinants in team sports by allowing to accurately measuring the activity demands. The aim of this study was to identify how the defensive pressure affects the time-motion variables and the technical actions in basketball.Methods: Twenty international male players(age: 16.05 ± 2.09 years, weight: 73.13 ± 8.10 kg, height: 183.10 ± 5.88 cm) played two 10 min basketball quarters, where they used a man-to-man 1/4-court defense until the 4th min(F1/4), changed to man-to-man full court(FULL) for 3 min and, from the 7th to the 10 th min returned to 1/4-court defense(S1/4). A computerized notational analysis was performed using Simi Scout and positional data were captured with the Ubisense Real Time Location System(mean sampling rate 3.74 ± 0.45 Hz per transmitter/player).Results: The time-motion variables presented similar results between defensive conditions, showing a total distance covered around 90 m/min.However, results suggested possible vertical jump impairments in S1/4 periods. There was more distance covered while jogging in the offensive court(38.15 ± 12.17 m/min offensive court vs. 32.94 ± 10.84 m/min defensive court, p < 0.05) and more distance covered while running in the defensive court(16.41 ± 10.27 m/min offensive court vs. 19.56 ± 10.29 m/min defensive court, p < 0.05).Conclusion: These results suggest how to improve task representativeness during specific conditioning or game-based training situations and also to help coaches' strategic decisions during the games.

A Constrained Time-Dependent K Shortest Paths Algorithm Addressing Overlap and Travel Time Deviation

Theoretical research often assumes all users arc homogeneous in their route choice decision and will always pick the route with the shortest travel cost,which is not necessarily the case in reality.This paper documents the research effort in developing a Constrained Time-Dependent K Shortest Paths Algorithm inorder to find K Shortest Paths between two given locations.The goal of this research is to provide sound route options to travelers in order to assist their route choice decision process,during which the overlap and travel time deviation issues between the K paths will be considered.The proposed algorithm balancing overlap and travel time deviation is developed in this research.A numerical analysis is conducted on the Tucson 1-10 network,the outcome of the case study shows that our proposed algorithm is able to find different shortest paths with a reasonable degree of similarity and close travel time,which indicates that the result of the proposed algorithm is satisfactory.

Effects of treatment time and temperature on the DC corona pretreatment performance of waste activated sludge

In order to improve the anaerobic digestion efficiency of waste activated sludge（WAS）,a pretreatment procedure should be carried out so as to disrupt the microbial cell structure,thus releasing intracellular organic matters.In this paper,a corona discharge triggered by a DC voltage was employed to pre-treat WAS for various time periods under different temperatures.The magnitude of the DC voltage was 4 k V at both negative and positive polarities.The changes in the soluble chemical oxygen demand,phosphorus and nitrogen content,and p H value within the WAS were utilized to estimate the pretreatment performance of the DC corona.It was found that with increasing treatment time,the pretreatment efficiency tends to be reduced.With increased temperature,the pretreatment efficiency appears to be better.It is suggested that the oxidative species and the active particles generated in the corona discharge play an important role in disrupting the microbial cell structure,which is dependent upon the treatment time and the temperature.

Exploring the impact of a coordinated variable speed limit control on congestion distribution in freeway

Over the past few decades, urban freeway congestion has been highly recognized as a serious and worsening traffic problem in the world. To relieve freeway congestion, several active traffic and demand management （ATDM） methods have been developed. Among them, variable speed limit （VSL） aims at regulating freeway mainline flow upstream to meet existing capacity and to harmonize vehicle speed. However, congestion may still be inevitable even with VSL implemented due to extremely high demand in actual practice. This study modified an existing VSL strategy by adding a new local constraint to suggest an achievable speed limit during the control period. As a queue is a product of the congestion phenomenon in freeway, the incentives of a queue build-up in the applied coordinated VSL control situation were analyzed. Considering a congestion occurrence （a queue build-up） characterized by a sudden and sharp speed drop, speed contours were utilized to demonstrate the congestion distribution over a whole freeway network in various sce- narios. Finally, congestion distributions found in both VSL control and non-VS control situations for various scenarios were investigated to explore the impact of the applied coordinated VSL control on the congestion distribution. An authentic stretch of V^hitemud Drive （I~~ID）, an urban freeway corridor in Edmonton, Alberta, Canada, was employed to implement this modified coordinated VSL control strategy; and a calibrated micro-simu- lation VISSIM model （model functions） was applied as the substitute of the real-world traffic system to test the above mentioned performance. The exploration task in this study can lay the groundwork for future research on how to improve the presented VSL control strategy for achieving the congestion mitigation effect on freeway.