Are we ready to embrace connected and self-driving vehicles? A case study of Texans

While connected, highly automated, and autonomous vehicles (CAVs) will eventually hit the roads, their success and market penetration rates depend largely on public opinions regarding benefits, concerns, and adoption of these technologies. Additionally, the introduction of these technologies is accompanied by uncertainties in their effects on the carsharing market and land use patterns, and raises the need for tolling policies to appease the travel demand induced due to the increased convenience. To these ends, this study surveyed 1088 respondents across Texas to understand their opinions about smart vehicle technologies and related decisions. The key summary statistics indicate that Texans are willing to pay (WTP) $2910, $4607, $7589, and $127 for Level 2, Level 3, and Level 4 automation and connectivity, respectively, on average. Moreover, affordability and equipment failure are Texans’ top two concerns regarding AVs. This study also estimates interval regression and ordered probit models to understand the multivariate correlation between explanatory variables, such as demographics, built-environment attributes, travel patterns, and crash histories, and response variables, including willingness to pay for CAV technologies, adoption rates of shared AVs at different pricing points, home location shift decisions, adoption timing of automation technologies, and opinions about various tolling policies. The practically significant relationships indicate that more experienced licensed drivers and older people associate lower WTP values with all new vehicle technologies. Such parameter estimates help not only in forecasting long-term adoption of CAV technologies, but also help transportation planners in understanding the characteristics of regions with high or low future-year CAV adoption levels, and subsequently, develop smart strategies in respective regions.     

Robust network pricing and system optimization under combined long-term stochasticity and elasticity of travel demand

Network pricing serves as an instrument for congestion management, however, agencies and planners often encounter problems of estimating appropriate toll prices. Tolls are commonly estimated for a single-point deterministic travel demand, which may lead to imperfect policy decisions due to inherent uncertainties in future travel demand. Previous research has addressed the issue of demand uncertainty in the pricing context, but the elastic nature of demand along with its uncertainty has not been explicitly considered. Similarly, interactions between elasticity and uncertainty of demand have not been characterized. This study addresses these gaps and proposes a framework to estimate nearest optimal first-best tolls under long-term stochasticity in elastic demand. We show first that the optimal tolls under the deterministic-elastic and stochastic-elastic demand cases coincide when cost and demand functions are linear, and the set of equilibrium paths is constant. These assumptions are restrictive, so three larger networks are considered numerically, and the subsequent pricing decisions are assessed. The results of the numerical experiments suggest that in many cases, optimal pricing decisions under the combined stochastic-elastic demand scenario resemble those when demand is known exactly. The applications in this study thus suggest that inclusion of demand elasticity offsets the need of considering future demand uncertainties for first-best congestion pricing frameworks.     

Influence of vehicular traffic on urban air quality – A case study of Hyderabad,India

In the present study, impact of vehicular traffic emissions on black carbon aerosol mass concentration, trace gases and ground reaching solar radiation were analyzed during nationwide truck strike of 5–12 January, 2009 over urban environment of Hyderabad, India. A significant reduction of about 57%, 60%, 40% and 50% was observed in black carbon, particulate matter, carbon monoxide and ozone respectively during nationwide truck strike period. Results of the study are important for source apportionment of pollutants as the strike created natural laboratory for studying the impact of diesel operated trucks on urban air quality.     

尼泊尔喜马拉雅地区塔玛科什3级水电工程——穿越软弱构造片岩修建隧洞所面临的挑战

由于尼泊尔喜马拉雅地区岩石强度较低,水平应力较高,因此在该软弱构造片岩带中开挖隧洞具有较大的挑战性.文章主要阐述装机容量为650 kW的塔玛科什3级水电工程17km长大跨度隧洞和大型地下发电厂房(这两项工程计划穿越夹杂有软弱构造片岩带的坚硬眼球状片麻岩区域)所面临的重大技术挑战.穿过片岩带开挖的隧洞长度约占总长的15％,由于此岩层带岩石强度较低,具有剪切特性和渗流现象,且水平应力较高,围岩挤压和隧洞塌方都是可预期的问题,因此该工程地下开挖主要关注的问题是大尺寸隧洞的可施工性和工期.为了解决可预期问题,进行了更进一步的研究,主要集中在隧洞最大可建尺寸、方位、减少围岩挤压的开挖方法,以及地下发电厂房稳定性和合理岩石支护设计上.研究发现,在构造片岩带中可施工的全断面隧洞最大尺寸可达9m,为改进的马蹄形(有弧形仰拱).通过对隧洞三种不同方位方案(即正交、斜交、平行)的分析发现,与叶理面正交的方案最佳,可减少围岩挤压近50％.     

Design and Testing of an Underwater Microscopewith Variable Objective Lens for the Study of Benthic Communities

Monitoring the ecology and physiology of corals, sediments, planktons, and microplastic at a suitable spatial resolution is of great importance in oceanic scien...     

Rationalizing Reliable Imputation Durations of Genetically Designed Time Delay Neural Network and Locally Weighted Regression Models

Abstract

Estimating missing values is known as data imputation. Previous research has shown that genetic algorithms (GAs) designed locally weighted regression (LWR) and time delay neural network (TDNN) models can generate more accurate hourly volume imputations for a period of 12 successive hours than traditional methods used by highway agencies. It would be interesting and important to further refine the models for imputing larger missing intervals. Therefore, a large number of genetically designed LWR and TDNN models are developed in this study and used to impute up to a week-long missing interval (168 hours) for sample traffic counts obtained from various groups of roads in Alberta, Canada. It is found that road type and functional class have considerable influences on reliable imputations. The reliable imputation durations range from 4–5 days for traffic counts with most unstable patterns to over 10 days for those with most stable patterns. The study results clearly show that calibrated GA-designed models can provide reliable imputations for missing data with ‘block patterns’, and demonstrate their further potentials in traffic data programs.     

Development of real-world driving cycle: Case study of Pune,India

The critical component of all emission models is a driving cycle representing the traffic behaviour. Although Indian driving cycles were developed to test the compliance of Indian vehicles to the relevant emission standards, they neglects higher speed and acceleration and assume all vehicle activities to be similar irrespective of heterogeneity in the traffic mix. Therefore, this study is an attempt to develop an urban driving cycle for estimating vehicular emissions and fuel consumption. The proposed methodology develops the driving cycle using micro-trips extracted from real-world data. The uniqueness of this methodology is that the driving cycle is constructed considering five important parameters of the time–space profile namely, the percentage acceleration, deceleration, idle, cruise, and the average speed. Therefore, this approach is expected to be a better representation of heterogeneous traffic behaviour. The driving cycle for the city of Pune in India is constructed using the proposed methodology and is compared with existing driving cycles.     

Park-and-ride lot choice model using random utility maximization and random regret minimization

This research aims to understand the park-and-ride (PNR) lot choice behaviour of users i.e., why PNR user choose one PNR lot versus another. Multinomial logit models are developed, the first based on the random utility maximization (RUM) concept where users are assumed to choose alternatives that have maximum utility, and the second based on the random regret minimization (RRM) concept where users are assumed to make decisions such that they minimize the regret in comparison to other foregone alternatives. A PNR trip is completed in two networks, the auto network and the transit network. The travel time of users for both the auto network and the transit network are used to create variables in the model. For the auto network, travel time is obtained using information from the strategic transport network using EMME/4 software, whereas travel time for the transit network is calculated using Google’s general transit feed specification data using a backward time-dependent shortest path algorithm. The involvement of two different networks in a PNR trip causes a trade-off relation within the PNR lot choice mechanism, and it is anticipated that an RRM model that captures this compromise effect may outperform typical RUM models. We use two forms of RRM models; the classical RRM and µRRM. Our results not only confirm a decade-old understanding that the RRM model may be an alternative concept to model transport choices, but also strengthen this understanding by exploring differences between two models in terms of model fit and out-of-sample predictive abilities. Further, our work is one of the few that estimates an RRM model on revealed preference data.

     

Configuration estimation method for preliminary cost of ships based on engineering bills of materials

Ships are complex engineering structures that are designed and built on the basis of technical experience. A shipowner will often be required to estimate the price of a new ship on the basis of the value of comparable ships identified in trade journals. Similarly, shipbuilders are often interested in estimating approximate costs during the tendering phase in order to determine whether a ship is likely to be competitive for a particular order. Thus, when designing a ship prior to having obtained a contract, one of the most important processes is the estimation of approximate costs, including materials, associated labor, and overhead. During this preliminary design phase, the design is temporary and subject to change based on variations in the shipowner’s requirements. Hence, quick and flexible responses are key during this period and an integral aspect of the competitive powers of the shipbuilder. Given this environment, we propose a “configuration estimation method.” Our method is based on the configuration design method that is widely used in three-dimensional (3D) computer aided design (CAD) systems. We assume that a product lifecycle management system is furnished and that the cost is then estimated via the configuration of the ship, using an engineering bill of materials (E-BOM). In referring to the E-BOM, we utilize technical parametric costs derived from similar ships built previously. Using the proposed method, it is possible to obtain an accurate list of materials from the quotation, as well as a detailed work assessment for labor costs and overhead rates, so that reliable cost estimates can be generated quickly and flexibly. To demonstrate the practical applicability and effectiveness of the proposed method, we implement the prototype of a shipbuilding configuration estimation system by using a Microsoft Structured Query Language database and an E-BOM from AVEVA Marine version 12.01, which is a representative CAD system for shipbuilding.     

A causal inference approach to measure the vulnerability of urban metro systems

Transportation - Transit operators need vulnerability measures to understand the level of service degradation under disruptions. This paper contributes to the literature with a novel causal...