A discrete choice framework for modeling and forecasting the adoption and diffusion of new transportation services

Major technological and infrastructural changes over the next decades, such as the introduction of autonomous vehicles, implementation of mileage-based fees, carsharing and ridesharing are expected to have a profound impact on lifestyles and travel behavior. Current travel demand models are unable to predict long-range trends in travel behavior as they do not entail a mechanism that projects membership and market share of new modes of transport (Uber, Lyft, etc.). We propose integrating discrete choice and technology adoption models to address the aforementioned issue. In order to do so, we build on the formulation of discrete mixture models and specifically Latent Class Choice Models (LCCMs), which were integrated with a network effect model. The network effect model quantifies the impact of the spatial/network effect of the new technology on the utility of adoption. We adopted a confirmatory approach to estimating our dynamic LCCM based on findings from the technology diffusion literature that focus on defining two distinct types of adopters: innovator/early adopters and imitators. LCCMs allow for heterogeneity in the utility of adoption for the various market segments i.e. innovators/early adopters, imitators and non-adopters. We make use of revealed preference (RP) time series data from a one-way carsharing system in a major city in the United States to estimate model parameters. The data entails a complete set of member enrollment for the carsharing service for a time period of 2.5 years after being launched. Consistent with the technology diffusion literature, our model identifies three latent classes whose utility of adoption have a well-defined set of preferences that are significant and behaviorally consistent. The technology adoption model predicts the probability that a certain individual will adopt the service at a certain time period, and is explained by social influences, network effect, socio-demographics and level-of-service attributes. Finally, the model was calibrated and then used to forecast adoption of the carsharing system for potential investment strategy scenarios. A couple of takeaways from the adoption forecasts were: (1) placing a new station/pod for the carsharing system outside a major technology firm induces the highest expected increase in the monthly number of adopters; and (2) no significant difference in the expected number of monthly adopters for the downtown region will exist between having a station or on-street parking.     

Where are the dangerous intersections for pedestrians and cyclists: A colocation-based approach

Pedestrians and cyclists are vulnerable road users. They are at greater risk for being killed in a crash than other road users. The percentage of fatal crashes that involve a pedestrian or cyclist is higher than the overall percentage of total trips taken by both modes. Because of this risk, finding ways to minimize problematic street environments is critical. Understanding traffic safety spatial patterns and identifying dangerous locations with significantly high crash risks for pedestrians and cyclists is essential in order to design possible countermeasures to improve road safety. This research develops two indicators for examining spatial correlation patterns between elements of the built environment (intersections) and crashes (pedestrian- or cyclist-involved). The global colocation quotient detects the overall connection in an area while the local colocation quotient identifies the locations of high-risk intersections. To illustrate our approach, we applied the methods to inspect the colocation patterns between pedestrian- or cyclist-vehicle crashes and intersections in Houston, Texas and we identified among many intersections the ones that significantly attract crashes. We also scrutinized those intersections, discussed possible attributes leading to high colocation of crashes, and proposed corresponding countermeasures.     

Multi-day activity-travel pattern sampling based on single-day data

Although it is important to consider multi-day activities in transportation planning, multi-day activity-travel data are expensive to acquire and therefore rarely available. In this study, we propose to generate multi-day activity-travel data through sampling from readily available single-day household travel survey data. A key observation we make is that the distribution of interpersonal variability in single-day travel activity datasets is similar to the distribution of intrapersonal variability in multi-day. Thus, interpersonal variability observed in cross-sectional single-day data of a group of people can be used to generate the day-to-day intrapersonal variability. The proposed sampling method is based on activity-travel pattern type clustering, travel distance and variability distribution to extract such information from single-day data. Validation and stability tests of the proposed sampling methods are presented.     

Impacts of information technology and urbanization on less-than-truckload freight flows in China: An analysis considering spatial effects

Understanding the relationship between socioeconomic factors and the Less-than-Truckload (LTL) freight flows is important for transportation planners and policy makers. This paper explores the impacts of information technology, urbanization on LTL freight flows by using a spatial autocorrelation model with freight flow data from a leading LTL company in China. The results show that all IT variables and urbanization variables have positive effects on freight flows. Distance, as expected, is negatively correlated with the freight flow volume. The application of the spatial autocorrelation model further shows that origin dependence, destination dependence and OD dependence are all significant, justifying the consideration of spatial interdependence. Finally, policy implications are discussed based on the estimated results. These findings shed light on the impacts of internet and urbanization on freight transportation, and contribute to the design of freight policies and the development of the LTL industry.     

Numerical simulations of seasonal and interannual variability of the Black Sea thermohaline circulation

The Black Sea general circulation is simulated by a primitive equation model with active free surface. The forcing is seasonally variable and is based on available climatic data. The model reproduces the main features of the Black Sea circulation, including the river discharge effects on the mean sea level and the Bosphorus outflow. Model results show that the simulated sea surface elevation increases in spring over the whole sea, reaching a maximum in the Danube delta area. In the same region, a minimum is observed in winter. The amplitude of the seasonal oscillations (about 8–12 cm over the whole basin) is of the same order of magnitude as the maximum horizontal variations (about 15–18 cm between the coastal areas and the basin interior). This strong signal formed mostly by river discharges, along with the seasonal variability in the other forcing functions and the local dynamics creates a well-pronounced interannual variability. The performance of the model in simulating the seasonal and interannual variability is critically analyzed, with a special attention on the cold intermediate water formation and the circulation in the upper 150 m. The simulations demonstrate that the source of intermediate waters is on the shelf, and that the water mass in the core of cold intermediate layer changes with time as a response to the periodic forcing at sea surface. This type of variability is characterized by pronounced interannual changes, proving that important differences could exist between water mass structure in different years, even when using identical atmospheric forcings each year.     

A heuristic model of bounded route choice in urban areas

There is substantial evidence to indicate that route choice in urban areas is complex cognitive process, conducted under uncertainty and formed on partial perspectives. Yet, conventional route choice models continue make simplistic assumptions around the nature of human cognitive ability, memory and preference. In this paper, a novel framework for route choice in urban areas is introduced, aiming to more accurately reflect the uncertain, bounded nature of route choice decision making. Two main advances are introduced. The first involves the definition of a hierarchical model of space representing the relationship between urban features and human cognition, combining findings from both the extensive previous literature on spatial cognition and a large route choice dataset. The second advance involves the development of heuristic rules for route choice decisions, building upon the hierarchical model of urban space. The heuristics describe the process by which quick, ‘good enough’ decisions are made when individuals are faced with uncertainty. This element of the model is once more constructed and parameterised according to findings from prior research and the trends identified within a large routing dataset. The paper outlines the implementation of the framework within a real-world context, validating the results against observed behaviours. Conclusions are offered as to the extension and improvement of this approach, outlining its potential as an alternative to other route choice modelling frameworks.     

On including travel time reliability of road traffic in appraisal

In many countries, decision-making on proposals for national or regional infrastructure projects in passenger and freight transport includes carrying out a cost–benefit analysis for these projects. Reductions in travel times are usually a key benefit. However, if a project also reduces the variability of travel time, travellers, freight operators and shippers will enjoy additional benefits, the ‘reliability benefits’. Until now, these benefits are usually not included in the cost–benefit analysis. To include reliability of travel or transport time in the cost–benefit analysis of infrastructure projects not only monetary values of reliability, but also reliability forecasting models are needed. As a result of an extensive feasibility study carried out for the German Federal Ministry of Transport, Building and Urban Development this paper aims to provide a literature overview and outcomes of an expert panel on how best to calculate and monetise reliability benefits, synthesised into recommendations for implementing travel time reliability into existing transport models in the short, medium, and long term. The paper focuses on road transport, which has also been the topic for most of the available literature on modelling and valuing transport time reliability.     

高速公路沥青路面施工参数变异性分析

对沥青路面施工过程中的控制指标如级配、油石比、压实度、厚度等进行了变异性统计分析，对所呈现出的变异性规律进行了探讨，对沥青路面施工控制中如何减少施工参数变异性具有一定参考价值。     

早期复极综合症心率变异性改变

观察了３１例早期复极综合症（ＥａｒｌｙｒｅＰｏｌａｒｉｚａｔｉｏｎｓｙｎｄｒｏｍｅ，ＥＲＳ）Ａ２９例正常对照组·Ｃ率变异（Ｈｅａｒｔｒａｔｅｖａｒｉａｂｉｌｉｔｙ，ＨＲＶ）五个时域指标的改变。结果发现，ＥＲＳ组相邻正常Ｒ－Ｒ问期差值的均万根ＧｅｑｕａｒｅｒｏｏｔｄｔｈｅｍｅａｎｓｑｕａｒｅｄｄｉｆｆｅｒｅｎｃｅｓｄＳｕｅ－ｃｅｓｓｉｖｅＮＮｉｎｔｅｒｖａｌｓ，ｒＭＳＳＤ）及相邻Ｗ常Ｒ－Ｒ问期至值大于５０ｍｓｉ６数占总Ｒ－Ｒ间期数的百分比（ＰｒｏＰｏｒｔｉｏｎｏｆＮＮ　ｉｎｔｅｒｖａｌｇｒｅａｔｅｒｔｈａｎ５０ｍｓ，ＰＮＮｓ。）较对照增高，其余指标无明显差异。提示ＥＲＳ存在到交感神经活性增高。ＥＲＳ·Ｏ率变异的特征性改变对于该症的诊断及鉴别诊断具有一定意义。     

Optimal biofuel supply chain design under consumption mandates with renewable identification numbers

The Renewable Identification Number (RIN) system is a tracking mechanism that enforces the U.S. Renewable Fuel Standard by monitoring obligated parties’ compliance with the biofuel consumption mandates. This paper incorporates the RIN system into the design of a biofuel supply chain that addresses independent decisions of non-cooperative farmers, biofuel manufacturers, and blenders. Game-theoretic models are developed to examine the impacts of the RIN system on individual stakeholders’ decisions (e.g., on farmland use, bio-refinery investment, biofuel production) and the competition between food and biofuel industries, in both a perfectly competitive scenario and a monopoly scenario. For the perfectly competitive scenario, Nash equilibrium can be obtained by solving a convex optimization problem. For the monopoly scenario, a bi-level Stackelberg leader–follower model is developed, from which we found that a rigid mandate on blenders may suppress the total biofuel production. To avoid such unintended consequences, a relaxed unit-RIN based penalty scheme is proposed and shown to improve the overall biofuel supply chain performance. Managerial insights are drawn from a numerical case study for the state of Illinois.