Income-contingent user preferences in policy evaluation: application and discussion based on multi-agent transport simulations

Standard economic policy evaluation allows the realization of projects if the aggregated economic benefit outweighs their costs. The use of one single aggregated welfare measure for evaluating and ranking projects has often been criticized for many reasons. A major issue is that differentiated effects on individuals or subgroups of the population are not taken into consideration. This leads to the need for transport planning tools that provide additional information for politicians and decision makers. The microscopic multi-agent simulation approach presented in this paper is capable of helping to design better solutions in such situations. In particular, it is shown that the inclusion of individual income in utility calculations allows a better understanding of problems linked to public acceptance. First, individual income-contingent utility functions are estimated based on survey data in order to describe human mobility behavior. Subsequently, using the MATSim framework, the implementation is tested in a test scenario. Furthermore, and going beyond Franklin (2006), it is shown that the approach works in a large-scale real world example. Based on a hypothetical speed increase of public transit, effects on the welfare distribution of the population are discussed. It is shown that the identification of winners and losers seems to be quite robust. However, results indicate that a conversion or aggregation of individual utility changes for welfare analysis is highly dependent on the functional form of the utility functions as well as on the choice of the aggregation procedure.     

A random utility maximization (RUM) based dynamic activity scheduling model: Application in weekend activity scheduling

The paper presents a modeling framework for dynamic activity scheduling. The modeling framework considers random utility maximization (RUM) assumption for its components in order to capture the joint activity type, location and continuous time expenditure choice tradeoffs over the course of the day. The dynamics of activity scheduling process are modeled by considering the history of activity participation as well as changes in time budget availability over the day. For empirical application, the model is estimated for weekend activity scheduling using a dataset (CHASE) collected in Toronto in 2002–2003. The data set classifies activities into nine general categories. For the empirical model of a 24-h weekend activity scheduling, only activity type and time expenditure choices are considered. The estimated empirical model captures many behavioral details and gives a high degree of fit to the observed weekend scheduling patterns. Some examples of such behavioral details are the effects of time of the day on activity type choice for scheduling and on the corresponding time expenditure; the effects of travel time requirements on activity type choice for scheduling and on the corresponding time expenditure, etc. Among many other findings, the empirical model reveals that on the weekend the utility of scheduling Recreational activities for later in the day and over a longer duration of time is high. It also reveals that on the weekend, Social activity scheduling is not affected by travel time requirements, but longer travel time requirements typically lead to longer-duration social activities.     

Analyzing the time frame for the transition from leisure-cyclist to commuter-cyclist

A recent survey reported that many commuter-cyclists had enjoyed leisure bicycling on a regular basis prior to becoming a commuter-cyclist. While bicycling for leisure, it is assumed that they considered various factors that led them to consider becoming commuter-cyclists. This study began with the question of how long it would take for a leisure-cyclist to become a commuter-cyclist, and it focused on the time that elapsed between leisure-cyclists transitioning to commuter-cycling. In order to analyze the time frame, it was hypothesized that the probability that a leisure-cyclist would become a commuter-cyclist at a certain time would be conditional on the duration that elapsed from the onset of leisure cycling till that time, which represents the “snowballing” or “inertial” dynamics of duration. A robust methodology, which is known as the “hazard model,” was adopted to accommodate such characteristics of a time period. In addition, various external covariates such as individual-specific characteristics, variables associated with the current or previous commuting mode, supply variables regarding bicycle facilities, and individual latent propensities were adopted to account for the duration of changes that would be generally applicable. As a result, many useful results were derived that could be used in fomenting policies to promote cycling to work. It was found that government should invest in establishing segregated lanes for leisure- and commuter-cyclists. It also turned out that a long distance to work hinders a leisure-cyclist from progressing to commuter-cycling. According to the results, young white-collar workers who live in high-rise apartments and enjoy intensive leisure-cycling in groups, are a good target toward whom promotions for commuter-cycling should be focused. However, an unfortunate development was that, when compared with car-commuters, it was found that transit-commuters are more likely to become commuter-cyclists.     

Reliability of Nanofluid Concentration on the Heat Transfer Augmentation in Engine Radiator

Nanofluids, the fluid suspensions of nanomaterial, became a promising fluid that is invoked when heat transfer increase is required. Using of nanofluids as a coolant in the engine radiators is a crucial topic for the thermal engines manufactrers due to the expected enhancement in the cooling process. In this study, Two nanofluids (Al₂O₃/water and CuO/water) flowing in a flat tube of radiator are investigated numerically to evaluate thermal and flow performance. The resizing process for the radiator is performed by using nanofluid instead of water flow. A significant reduction in the radiator volume is achieved due to marked improvement in the heat transfer performance while, the required pumping power after this reduction in the volume is increased over that needed for base fluid. The normalized heat transfer (heat transfer to the pumping power) is found to be a function of both Reynolds number and nanofluid concentration ratio while the ratio of the normalized heat transfer is found to be dependent only on the nanofluid concentration ratio. These dependencies are formulated as general correlations.     

Comparative investigation on the aerodynamic effects of combined use of underbody drag reduction devices applied to real sedan

To reduce the aerodynamic drag, the performance of the underbody aerodynamic drag reduction devices was evaluated based on the actual shape of a sedan-type vehicle. An undercover, under-fin, and side air dam were used as the underbody aerodynamic drag reduction devices. In addition, the effects of the interactions based on the combination of the aerodynamic drag reduction devices were investigated. A commercial sedan-type vehicle was selected as a reference model and its shape was modeled in detail. Aerodynamic drag was analyzed by computational fluid dynamics at a general driving speed on highway of 120 km/h. The undercover reduced the slipstream area through the attenuation of the longitudinal vortex pair by enhancing the up-wash of underflow, thereby reducing the aerodynamic drag by 8.4 %. The under-fin and side air dam showed no reduction in aerodynamic drag when they were solely attached to the actual complex shape of the underbody. Simple aggregation of the effects of aerodynamic drag reduction by the individual device did not provide the accurate performance of the combined aerodynamic drag reduction devices. An additional aerodynamic drag reduction of 2.1 % on average was obtained compared to the expected drag reduction, which was due to the synergy effect of the combination.     

Safety-oriented bicycling and traffic accident involvement

Many U.S. bicyclists are killed or injured in traffic accidents annually. Based on analysis of available research and published reports on traffic accidents, it was theorized that adherence to six safety-oriented on-road bicycling practices will reduce involvement in traffic accidents. This study investigated whether adherence to these safety-oriented bicycling practices is associated with reduced involvement in traffic accidents. U.S. adult bicycle riders responded to an anonymous on-line survey covering bicycling practices, recent traffic accident experiences, and potential confounding variables. Participants were recruited via on-line announcements to bicycling-related organizations. The results suggested that neither vigilance nor cautiousness will protect adult bicycle riders from traffic accident involvement, but predictability might reduce accident risk, particularly for younger adults. Results should be interpreted cautiously due to limitations in the study methodology. Further research of this type is warranted.This paper is a re-formulation of research originally presented as a conference paper and oral presentation at the International Cycling Safety Conference in Bologna, Italy, 3–4 November 2016.     

Development of a self-driving car that can handle the adverse weather

Lane and road recognition are essential for self-driving where GPS solution is inaccurate due to the signal block or multipath in an urban environment. Vision based lane or road recognition algorithms have been studied extensively, but they are not robust to changes in weather or illumination due to the characteristic of the sensor. Lidar is a sensor for measuring distance, but it also contains intensity information. The road mark on the road is made to look good with headlight at night by using a special paint with good reflection on the light. With this feature, road marking can be detected with lidar even in the case of changes in illumination due to the rain or shadow. In this paper, we propose equipping autonomous cars with sensor fusion algorithms intended to operate in a different weather conditions. The proposed algorithm was applied to the self-driving car EureCar (KAIST) in order to test its feasibility for real-time use.     

A Practical SSL Server Performance Improvement Algorithm Based on Batch RSA Decryption

The secure socket layer/ transport layer security（SSL/TLS） handshake protocol uses public key cryptographic algorithms such as RSA for key establishment. Typically, public key cryptographic algorithm is computational intensive due to the modular multiplications. Therefore, SSL/TLS servers often become swamped while performing public key decryptions when the simultaneous requests increase quickly. A batch RSA decryption algorithm was proposed. The novel algorithm provides the reasonable response time and optimizes server performance significantly. The decryption speedup is proportional to the batch size b, for instance, the speedup factor is 4, while in Shacham＇s scheme the acceleration rate is only 2.5 when b = 4.