A disaggregate analysis of factors influencing port selection

In this paper are presented the results of a multinomial logit model used to explain the selection of a port for each shipment exported from the US in December 1999. The model is estimated for combined shipments, and one model is estimated for each of four commodity types (bulk, foods, fabrics, manufactured). Results are compared across commodities to find the significance of the distance in either direction, the frequency of sailings, and the average size of vessels sailing along a route. Results of the estimation do not match those that would be expected. Potential reasons are given for such variations, along with numerous methods by which the modelling could be improved. Methods for improvement suggested include structuring of the model to represent more accurately the competitive scenario of each port, constraining the choice set for each shipments from characteristics of the carrier, and the commodity.     

Estimating multimodal transit ridership with a varying fare structure

This paper studies public transport demand by estimating a system of equations for multimodal transit systems where different modes may act competitively or cooperatively. Using data from Athens, Greece, we explicitly correct for higher-order serial correlation in the error terms and investigate two, largely overlooked, questions in the transit literature; first, whether a varying fare structure in a multimodal transit system affects demand and, second, what the determinants of ticket versus travelcard sales may be. Model estimation results suggest that the effect of fare type on ridership levels in a multimodal system varies by mode and by relative ticket to travelcard prices. Further, regardless of competition or cooperation between modes, fare increases will have limited effects on ridership, but the magnitude of these effects does depend on the relative ticket to travelcard prices. Finally, incorrectly assuming serial independence for the error terms during model estimation could yield upward or downward biased parameters and hence result in incorrect inferences and policy recommendations.     

Group-based geographic information systems for transportation improvement site selection

Transportation improvement site selection exemplifies transportation decision making that is collaborative in nature and geographically based. Such decision-making is part of a broad societal trend toward shared and participatory discussions about public investment. Perspectives from three different transportation decision contexts in the Puget Sound Region of Washington State, a regional council, a county government and a public–private Coalition group, are combined with a literature review to develop a decision task model that summarizes the need for information technology support during transportation improvement site selection. The task model guides the development of a decision support system requirement specification that outlines integrated information capabilities provided by geographic information system (GIS) and group support system (GSS) technologies. Together, GIS and GSS capabilities contribute to evolving group-based GIS. The kinds of capabilities a group-based GIS could offer in addressing the needs are identified. A report on the use of a prototype, group-based GIS called Spatial Group Choice highlights the possibilities in an inter-organizational coalition decision context. The conclusions discuss needs for future technology developments and social–behavioral science studies on these developments.     

A novel arterial travel time distribution estimation model and its application to energy/emissions estimation

Arterial travel time information is crucial to advanced traffic management systems and advanced traveler information systems. An effective way to represent this information is the estimation of travel time distribution. In this paper, we develop a modified Gaussian mixture model in order to estimate link travel time distributions along arterial with signalized intersections. The proposed model is applicable to traffic data from either fixed-location sensors or mobile sensors. The model performance is validated using real-world traffic data (more than 1,400 vehicles) collected by the wireless magnetic sensors and digital image recognition in the field. The proposed model shows high potential (i.e., the correction rate are above 0.9) to satisfactorily estimate travel time statistics and classify vehicle stop versus non-stop movements. In addition, the resultant movement classification application can significantly improve the estimation of traffic-related energy and emissions along arterial.     

COUPE LIFE BRILLIANCE AUTO Zhonghua Coupe 18TAT

车能带给人们别样生活,而一台个性十足、完全不同于其他车款的Coupe,更将全然颠覆你的生活态度!对于尚未有车的人群来说,车是—个梦,而当美梦成真之时,亦揭开有车生活的序幕;同理,如果你够胆魄选择了一台Coupe作为你的座驾,恭喜你,你会拥有的不止是高频回头率、高调的心态,更将迎来以Coupe为核心的另类有车生活,而这种生活的真谛也许只有你,Coupe车主才有资格说三道四。     

A Real-Time Parking Prediction System for Smart Cities

A methodological framework for multiple steps ahead parking availability prediction is presented. Two different types of predictions are provided: the probability of a free space to continue being free in subsequent time intervals, and the short-term parking occupancy prediction in selected regions of an urban road network. The available data come from a wide network of on-street parking sensors in the “smart” city of Santander, Spain. The sensor network is segmented in four different regions, and then survival and neural network models are developed for each region separately. Findings show that the Weibull parametric models best describe the probability of a parking space to continue to be free in the forthcoming time intervals. Moreover, simple genetically optimized multilayer perceptrons accurately predict region parking occupancy rates up to 30 minutes in the future by exploiting 1-minute data. Finally, the real time, Web-based, implementation of the proposed parking prediction availability system is presented.     

Developing an activity-based trip generation model for small/medium size planning agencies

The primary shortcoming of traditional four-step models is that they cannot capture derived travel demand behaviors. However, travel demand modeling (TDM) is an essential input for urban transportation planning. TDM needs to be highly precise and accurate by integrating the accurate base year estimation along with suitable alternatives. Currently, activity-based models (ABMs) have been developed mostly for large metropolitan planning organizations (MPO), whereas smaller/medium-sized MPOs typically lack these models. The main reason for this disparity in ABM development is the complexity of the models and the cost and data requirements needed. We posit however that smaller MPOs could develop ABMs from traditional travel surveys. Therefore, the specific aim of this paper is to develop a probabilistic home-based destination activity trip generation model considering travel time behavior. Results show that the developed model can significantly capture the actual number of trip generations.     

Potential safety benefits of lane departure prevention technology

Motor vehicles with advanced safety technologies are rapidly entering the marketplace and the impact of new features are transforming safety on roadways. Among the several safety related technologies currently available in the market, this paper aims to forecast the reduction in crashes with gradual adoption of vehicles with lane departure prevention (LDP) technology. Crash data for the state of Alabama from 2014 to 2016 were used to evaluate the safety benefits of LDP technology. In Alabama, 75% of single-vehicle crashes are the result of lane departure. A 20% effective LDP system implies, whereby an LDP system would prevent a vehicle from exiting a roadway on 20% of applicable instances, would reduce 2.7% and 16.4% of the relevant single-vehicle lane departure (SVLD) crashes by 2020 and 2045 respectively. With increase in the effectiveness of the technology, a greater reduction in crashes was observed. With 100% effectiveness, this technology can prevent 66.5% of SVLD crashes by year 2045. This study presents the first estimations of the number of crashes that could be reduced using LDP and therefore could have significant impacts on public and industry adoption rates of the technology. The results of this study influence policy making and regulatory approaches to improving motor vehicle safety and further recommend education and outreach activities to increase awareness on the benefits of LDP technology.     

Stated preference modelling of intra-household decisions: Can you more easily approximate the preference space?

While the study of choices focuses primarily on the individual decision maker, there is growing interest in the examination of the choices made by groups. Much of the research into the choices of multiple decision makers has revealed that they differ significantly to those of individuals. In this study of household vehicle choice we similarly compare individual choices to group choices and support this finding. Consequently any research into choices that involve groups should acquire data from those groups. In this paper we show how this may be done via an interactive agency choice experiment which makes the individual preferences endogenous to the choice of the group. This method and many like it, however, involve significant time, incentive and administrative costs that often make such studies prohibitive. In this paper we also compare another class of model, minimum information group inference, which is designed to provide an overview of the likely group choice and while not having the specificity of detail as other methods, has the advantage of being much easier and cheaper to implement and is perhaps the only methodology that can be employed when it is not feasible for respondents to interact. We find that this method is a good approximation of group choice, mapping the preference space over which group choice is likely to be located.

     

Applications of Random-Utility-based Multi-region Input–Output Models of Transport and the Spatial Economy

Abstract

An introduction to random-utility-based multiregional input–output models used for the purpose of spatial economic and transport interaction modelling is provided. The main methodological developments and important results of a dozen applications from the years 1996–2013 are described. This is followed by an outlook of potential future directions. Further research is mainly needed in five areas: (a) overall validation of the method, perhaps through back-casting applications on infrastructure plans with observed trade impacts; (b) extensions of trade coefficient models to add realism and improve accuracy; (c) the use of multi-scale modelling to capture interdependencies between geographical scales and to improve the representation of exports and imports; (d) improvements in the representation of price effects, as well as innovation and technological progress, by way of variable technical coefficients; and (e) a deeper investigation of the algorithm used to include elastic selling prices.