Clean energy vehicles (CEVs) such as EV or PHV should be promoted for the reduction of greenhouse gas emissions in the transportation sector. However, it is important to note that social interactions affect the promotion of CEVs. Therefore, a multiagent simulation system for promoting CEVs has been developed in the present study from the perspective of not only concern for the environment but also for local interaction processes in terms of social conformity. Intention of holding CEV is described with the database of the questionnaire survey about purchasing vehicles. The social network of the artificial society is described as a small-world network model. The time series changes of the numbers of clean energy vehicles and the volume of greenhouse gas emissions are estimated by the proposed multiagent simulation. Finally, it can be concluded that the proposed multiagent simulation with local interaction is useful for promoting the planning of CEVs.

The linear viscoelastic (LVE) limits and rheological properties of bitumen-filler mastics were determined using a dynamic shear rheometer (DSR). Strain and frequency sweeps were performed on mastic specimens over a wide range of test temperature and frequency conditions. The use of an empirical algebraic equation of the Christensen and Anderson model (CA model) was used to characterize the LVE rheological behavior of the mastics in this study. Test results showed that the LVE limits for the concentrated suspension mastics were more restrictive than those for the dilute suspension mastics. Filler particles were likely to cause a hydrodynamic interaction in the concentrated suspension system. A significant difference in stiffening effect for the concentrated suspension mastics was observed due to the filler effective volume as well as the interparticle interaction. For the dilute suspension mastics, the difference in filler stiffening effect was minimal irrespective of filler type in this study.

Porous pavements are often used as a means to improve skid resistance during wet weather. During the design and construction phases, porosity is often used as the single parameter representing porous pavement drainage capacity. However, it is insufficient to use this parameter to predict frictional performance on porous pavements because pavement mix with different aggregate sizes and yet having identical porosity values can yield significantly different skid resistance behaviors. This paper presents a numerical framework to analyze the influence of aggregate size on porous pavement skid resistance. It was found that despite having identical porosity, pavement mix with larger aggregates can provide higher permeability value, which consequently reduces the water film thickness on the pavement surface under a given rainfall intensity and relieves the hydrodynamic pressure buildup underneath the tire. Porous pavements constructed with larger-size aggregates for a given porosity are found to exhibit better skid resistance performance.

Existing signal control strategies do not consider pedestrian flows in optimizing signal parameters, which may impose significant delays on pedestrians. This study aims to investigate the rationality and effectiveness of designing signal coordination for pedestrians. A numerical case study in Japan is analyzed. Field survey is conducted to collect the geometric characteristics, signal timings and vehicular traffic condition information. In a parallel approach, the performances of signal coordination for vehicular and pedestrian traffic are estimated by using the vehicular traffic simulation tool Synchro/SimTraffic and the pedestrian simulation tool NOMAD. The results showed that the coordination for the major pedestrian flow led to a significant reduction in average delay (15%). Generally, it is concluded that the effectiveness of pedestrian signal coordination is not guaranteed but depends on the relationship between pedestrian platoon dispersion and the signal cycle length.

The objective of this paper is to evaluate travel time estimation techniques for off-line and on-line applications using traffic speed. The study area was the 8.1-km Chalerm Mahanakorn Expressway Daokanong - Port section in Bangkok. Traffic speeds were collected at 7 stations. Average traffic volume of 6,140 vph (vehicles per hour) was observed during the 6:00am to 22:00pm period. Four offline methods were used to convert speed into travel time for each link, namely: mid-point, section average, weighted section average, and San Antonio's (lower speed). To aggregate link travel time into route travel time, the instantaneous and time slice methods were considered. The estimated travel times were then compared with the actual travel time observed from 1,632 vehicles. The results indicate that the accuracy depends on the time period and level of traffic congestion. Considering route travel time, the time slice method gives a slightly better accuracy than the instantaneous method. Comparing the offline (all data present) and online (short-term forecast of speed in the next time slice(s)) travel time estimation, the two methods yield similar accuracy. The findings of the research imply that the practical travel time estimation may need several methods suitable for each time period. The improved estimation can increase the accuracy over a single method, thereby decreasing the MAPE (mean absolute percentage error) from 14-20% to 11%.

This paper outlines the development of an analytical model of multimodal, multicommodity freight transportation in Indonesia. The model is used to evaluate strategic planning of inter-island freight transportation. The demand for inter-island freight transportation is estimated using econometric demand models which account for the socioeconomic characteristics of the regions and commodity types. The supply side is concerned with modeling the inter-island transportation network, including links, such as the land access network, and transfer points which are connected by the links, and a generalized cost function. System optimization is obtained by identifying the combination of links and transfers that minimize the total generalized transport cost. The model is applied to the evaluation of the impacts of several policy scenarios which contribute to the improvement of the inter-island freight transportation network. The model has shown its capability of estimating system costs of transportation network that can be utilized to assess the policy implications.

This study attempts to evaluate the heterogeneous influence of driving propensity on driving intention on expressways under the provision of ITS (Intelligent Transportation Systems)-based and/or traditional traffic warning measures. An Internet-based questionnaire survey was conducted to collect relevant data including drivers' actual driving propensity and their stated driving behavior (i.e., driving intention) under different combinations of information provisions and existing safety facilities. Heterogeneity in the driving intention is taken into account by developing a latent class ordered-response probit model. As a result, it is confirmed that the warning information with voice and image through ITS technology (car-navigation) is a more effective measure than existing safety facilities to improve their awareness for safe driving; it is statistically confirmed that the developed model shows different driving intention by heterogeneities of drivers' driving propensities when same traffic warning information is provided on expressway.

The impact of vehicle emissions on the global climate has drawn increasing concern in the past few decades. Patterns of housing development determine travel behaviors, thus affecting transport-related greenhouse gas emissions. Here, a bi-level model is established to describe the relationships among housing allocation, traffic volume, and CO2 emissions using a continuum modeling approach. The user-equilibrium condition is achieved in the lower-level, and the minimum CO2 emissions are obtained by optimization the housing allocation in the upper-level. A hypothetical city is considered with one central business district (CBD) and a road network that is densely distributed outside of the CBD. Several commuter classes with different values of time are considered. The finite element method, the Newton-Raphson algorithm, and the convex combination approach are applied to solve the constrained optimization problem established in the bi-level model. A numerical example is given to demonstrate the effectiveness of the method.

This study examines the performance of genetic algorithms (GAs) on solving a pickup and delivery vehicle routing problem, typically faced by home delivery service providers. A number of combinations of GA's three main algorithmic operators, namely, selection, crossover, and mutation is implemented, and the data envelopment analysis (DEA) is adopted to evaluate and rank these various combinations of GA operators. The numerical results demonstrate that DEA is appropriate in determining the efficient combinations of GA operators. Among the combinations under consideration, the combination using tournament selection and simple crossover is generally more efficient. The findings also contribute to algorithm development and evaluation of vehicle routing problems from the operations research perspective.