A Continuous Taxi Pickup Path Recommendation under The Carbon Neutrality Context

In the context of the carbon neutrality target, carbon reduction in the daily operation of the transportation system is more important than that in productive activities. There are few travel services that can quantify low-carbon travel, with a lack of effective low-carbon travel tools to guide transportation behavior. On-demand access to taxi services can effectively reduce the additional carbon emissions caused by cruising, which in turn increases efficiency in urban mobility with a reduced taxi fleet scale. For individual taxis, they lack macroscopic horizon in their choice of passenger pickup paths. The selected travel path based on personal operational experience or real-time location is limited by local optimization when making path decisions. In this work, we proposed a macro-path recommendation method to assist the taxi pickup path selection to accelerate the transformation of the taxi system towards low-carbon sharing. First, an adaptive learning spatiotemporal neural network was used to predict the coarse-grained distribution of potential trips. Next, the trajectory sharing graph was constructed based on the potential trips distribution to reallocate the taxi orders for the continuous pickup path optimization. As a result, the continuous pickup path balanced the relation between travel demands and taxi supply, improving the economic and environmental benefits of taxi operation and contributing to the goal of carbon neutrality. We conducted experiments on the Chengdu city ride-hailing dataset. Compared with the current status of taxi operations, the solution shows improvements in both the scale of taxi services and order gain.

Factors associated with emerging multimodal transportation behavior in the San Francisco Bay Area

This paper identifies the influence of demographic, local transportation environment, and individual preferences for transportation attributes on multimodal transportation behavior in an urban environment with emergent transportation mode availability. Multimodality is the use of more than one mode of transportation during a given timeframe. Multimodality has been considered a key component of sustainable and efficient transportation systems, as this travel behavior can represent a shift away from personal vehicle use to more sustainable transportation modes, especially in urban environments with diverse transportation systems and emergent shared transportation alternatives (e.g., carsharing, ridehailing, bike sharing). However, it is unclear what factors contribute towards people being more likely to exhibit multimodal transportation behavior in modern urban environments. We assessed commuting behavior based on a survey administered in the San Francisco Bay Area according to whether residents commuted (i) exclusively by vehicle, (ii) by a mix of vehicle and non-vehicle modes, or (iii) exclusively by non-vehicle modes. A classification tree approach identified correlations between commuting classes and demographic variables, preferences for transportation attributes, and location-based information. The characterization of commuting styles could inform regional transportation policy and design that aims to reduce vehicle use by identifying the demographic, preference, and location-based considerations correlated with each commuting style.

FACTORS FORMING CITIZENS’ TRANSPORTATION BEHAVIOR: THE CASE OF ST. PETERSBURG

Successful implementation of public transportation reform cannot be achieved without studying transportation behavior of citizens. The results of an empirical analysis of the behavior of St. Petersburg residents presented in this article make it possible to assess what transportation policies will be successful in attracting car users to travel by public transport. The aim of the study was to identify the patterns of transportation behavior in St. Petersburg: attractiveness of various transportation modes, mode choice factors as well as satisfaction with public transport and transportation policies aimed at stimulating the use of public transport. The research methodology includes survey of the population and the analysis of descriptive information from the data obtained. In addition, principal component analysis was applied for travel factors grouping. The results of this study may be used in elaboration of transportation policies aimed at changing transportation behavior of the citizens. The main finding of this study is the relative importance of trip time for public transport in contrast to private car.

Personas: A Market Segmentation Approach for Transportation Behavior Change

This paper proposes a market segmentation method applied in the field of transportation behavior change using GPS trajectories and socio-demographic data collected from the advanced demand management system “GoEzy” designed by Metropia. User attributes are extracted using several statistical methods such as dynamic time warping, density-based spatial clustering of applications with noise (DBSCAN), and signal processing method to infer users’ sensitivity to incentives, temporal, and spatial travel patterns. Ten personas were generated by K-means clustering, representing different types of people with various travel patterns and sensitivity to incentives. The experiment was conducted on 24 new users to test if the persona could be used as a tool to predict their willingness to change. The results showed that after creating personas for new users and providing them with new incentives, their modified departure time pattern according to the new incentives matched expectations from analysis of the 10 personas.

A Molecular Dynamics Study of Heat Transfer Enhancement during Phase Change from a Nanoengineered Solid Surface

This study investigates the enhancement of the rate of evaporation from a nanoengineered solid surface using non-equilibrium molecular dynamics simulation. Four different types of surface modifications were introduced to examine the thermal transportation behavior. The surface modification includes: (1) transformation of surface wetting condition from hydrophobic to hydrophilic, (2) implementing nanostructures on the smooth surface, (3) cutting nano slots on the smooth surface and (4) introducing nano-level surface roughness. Evaporation behavior from the same effective surface area was also studied. The simulation domain consisted of three distinct zones: solid base wall made of copper, a few layers of liquid argon, and a vapor zone made of argon. All the nano-level surface modifications were introduced on the solid base surface. The few layers of liquid argon representing the liquid zone of the domain take heat from the solid surface and get evaporated. Outside this solid and liquid zone, there is argon vapor. The simulation began at the initial time t = 0 ns and then was allowed to reach equilibrium. Immediately after equilibrium was achieved on all three-phase systems, the temperature of the solid wall was raised to a higher value. In this way, thermal transportation from the solid wall to liquid argon was established. As the temperature of the solid wall was high enough, the liquid argon tended to evaporate. From the simulation results, it is observed that during the transformation from hydrophobic to hydrophilic conditions, enhancement of evaporation takes place due to the improvement of thermal transportation behavior. At the nanostructure surface, the active nucleation sites and effective surface area increase which results in evaporation enhancement. With nano slots and nano-level surface roughness, the rate of evaporation increases due to the increase of solid-liquid contact area and effective surface area.

Improved Efficiency and Carrier Dynamics Transportation Behavior in Perovskite Solar Cells with CuInS2 Quantum Dots as Hole-Transport Materials

Inorganic quantum dots (QDs) based hole transport materials (HTMs) have approved their potentials in perovskite solar cells (PSCs). In this work, CuInS2 quantum dots (CIS QDs) were applied as HTMs...

Households’ Intended Evacuation Transportation Behavior in Response to Earthquake and Tsunami Hazard in a Cascadia Subduction Zone City

Earthquakes along the Cascadia subduction zone would generate a local tsunami that could arrive at coastlines within minutes. Few studies provide empirical evidence to understand the potential behaviors of local residents during this emergency. To fill this knowledge gap, this study examines residents’ perceptions and intended evacuation behaviors in response to an earthquake and tsunami, utilizing a survey sent to households in Seaside, OR. The results show that the majority of respondents can correctly identify whether their house is inside or outside a tsunami inundation zone. Older respondents are more likely to identify this correctly regardless of any previous disaster evacuation experience or community tenure. The majority of respondents (69%) say they would evacuate in the event of a tsunami. Factors influencing this choice include age, motor ability, access to transportation, and trust in infrastructure resiliency or traffic conditions. While the City of Seaside actively promotes evacuation by foot, 38% of respondents still state they would use a motor vehicle to evacuate. Females and older respondents are more likely to evacuate by foot. Respondents with both higher confidence in their knowledge of disaster evacuation and higher income are more likely to indicate less time needed to evacuate than others. Generally, respondents are more likely to lead rather than follow during an evacuation, especially respondents who report being more prepared for an evacuation and who have a higher perceived risk. This study showcases a unique effort at empirically analyzing human tsunami evacuation lead or follow choice behavior.