A general corridor model for designing plug-in electric vehicle charging infrastructure to support intercity travel

This paper proposes to optimally configure plug-in electric vehicle (PEV) charging infrastructure for supporting long-distance intercity travel using a general corridor model that aims to minimize a total system cost inclusive of infrastructure investment, battery cost and user cost. Compared to the previous work, the proposed model not only allows realistic patterns of origin–destination demands, but also considers flow-dependent charging delay induced by congestion at charging stations. With these extensions, the model is better suited to performing a sketchy design of charging infrastructure along highway corridors. The proposed model is formulated as a mixed integer program with nonlinear constraints and solved by a specialized metaheuristic algorithm based on Simulated Annealing. Our numerical experiments show that the metaheuristic produces satisfactory solutions in comparison with benchmark solutions obtained by a mainstream commercial solver, but is more computationally tractable for larger problems. Noteworthy findings from numerical results are: (1) ignoring queuing delay inducted by charging congestion could lead to suboptimal configuration of charging infrastructure, and its effect is expected to be more significant when the market share of PEVs rises; (2) in the absence of the battery cost, it is important to consider the trade-off between the costs of charging delay and the infrastructure; and (3) building long-range PEVs with the current generation of battery technology may not be cost effective from the societal point of view.     

Flexible long range planning using low cost information

Contemporary transport planning requires a flexible modelling approach which can be used to monitor the implementation of a long term plan checking regularly its short term performance with easily available data; the original model is periodically updated using low cost information and this allows the evaluation of the changes to the plan which may be required. Such an approach requires models suited to regular updating and to the use of data from different sources. Models to update trip matrices from traffic counts have been available for some time; however, the estimation and/or updating of other model stages with low cost data has escaped analytical treatment. The paper discusses this idea and formulates the updating problem for an example involving a joint destination/mode choice model under various assumptions about the nature of the available data. Analytical solutions are proposed as well as some general conclusions.requests for offprints     

Evaluation of a transportation system employing autonomous vehicles

The problem of studying public transportation systems with autonomous vehicles is challenging because of behavioral differences that make existing models poorly fit and the technical difficulties involved in studying large autonomous systems operating on a grand scale. In this paper, we propose the following: (i) an autonomous transportation network setting; (ii) a method for modeling autonomous vehicles in simulation; and (iii) a high‐performance simulation platform that allows analysis and visualization of transportation technologies. Results from microsimulation confirm theoretical benefits and improvements from employing autonomous systems in an example setting and highlight the platform's general ability to allow researchers to implement novel transportation systems and study the cost benefit variations occurring between them. Copyright © 2017 John Wiley & Sons, Ltd.     

Control system design for an individual signalized junction

The traffic signal settings for a single road junction have been often evaluated by mathematical programming techniques. This paper proposes a new approach to the problem which allows all the regulation variables to be incorporated into a Binary-Mixed-Integer- Linear-Programming model. This general model permits some of the limitative assumptions involved in other formulations of the problem based on the stage matrix to be removed. The model can be easily solved obtaining a fast computation of the globally optimal control system design. A detailed treatment is given for the particular structure of the mathematical programming schemes obtained by considering delay minimization, capacity reserve maximization, or cycle time minimization as the objective.     

A translog cost function of the integrated air freight business: The case of FedEx and UPS

This paper analyzes the cost structure of the integrated air freight business by means of a translog cost function. This allows to extend knowledge on the supply side and to examine if strategies of integrators are consistent with cost structure. The cost function is based on quarterly time-series data from 1990 to 2010 for FedEx and UPS. A total and a variable model are estimated. In addition, a static as well as a dynamic approach is followed. We find that integrators exhibit strong scale and density economies in the short and the long term. This result is in line with the aggressive expansion and cooperation strategies pursued by integrators. Our results indicate that the concentration in the integrated air freight industry will continue: a concern for industry actors and regulatory agencies.     

Market introduction strategies for alternative powertrains in long-range passenger cars under competition

Alternative powertrains are considered as a promising option to significantly reduce CO₂ emissions from passenger cars. One major prerequisite is their successful market introduction. In this paper, we present a system dynamics model that allows for the evaluation of strategies for the market introduction of alternative powertrain technologies in long-range passenger cars (⩾400 km) under competition. The model considers two competing manufacturers, one first-mover and one follower, each introducing plug-in hybrids and fuel cell electric vehicles according to exogenously defined strategies, which comprise timing, pricing, and technology parameters. The manufacturers can learn from each other due to technology spillover, leading to cost reductions of the powertrains. We use an exemplary dataset for the German car market to study the manufacturers’ influence on the market success of alternative powertrains as well as the underlying mechanisms. The results indicate that in general more competition leads to higher market shares of alternatively powered vehicles and thus allows for a higher reduction of emissions. However, this might cause decreasing profits for both manufacturers, especially if the follower pursues an aggressive pricing strategy when entering the market to gain market shares from its competitor. Also, technology spillover has a positive effect on the market penetration. This particularly holds true for a low level of technology experience where high cost reductions can be achieved and for fuel cell electric vehicles where the costs of the powertrain are much higher compared to plug-in hybrids.     

Estimating origin-destination specific railroad marginal operating cost functions

We develop a model of the costs of railroad operations which allows examination of the effects of network and input price changes, and which allows us to estimate marginal operating costs on an origin-destination basis. A primary potential use of this model is pricing of specific services. Economic theory is used to specify a marginal cost function which is estimated using data from the network analysis. This provides a convenient summary of the information in the network model, and could be used to investigate the impacts of changes in prices of input, such as fuel, on marginal operating costs.     

柔性桥塔结构环境振动测试研究

文章以某大桥柔性桥塔结构环境振动测试为例,阐述了桥塔结构环境振动测试的一般步骤和技术细节,并采用时域随机子空间识别方法（SSI）对桥塔动力特性（振动模态参数）进行了识别分析。经验证,识别分析结果与理论计算值能较好吻合。     

Advanced pricing and rationing policies for large scale multimodal networks

The applying of simplified schemes, such as cordon pricing, as second-best solution to the toll network design problem is investigated here in the context of multiclass traffic assignment on multimodal networks. To this end a suitable equilibrium model has been developed, together with an efficient algorithm capable of simulating large scale networks in quite reasonable computer time. This model implements the theoretical framework proposed in a previous work on the toll optimization problem, where the validity of marginal cost pricing for the context at hand is stated. Application of the model to the real case of Rome shows us, not only that on multimodal networks a relevant share (up to 20%) of the maximum improvements in terms of social welfare achievable with marginal cost pricing can in fact be obtained through cordon pricing, but also that in practical terms rationing is a valid alternative to pricing, thus getting around some of the relevant questions (theoretical, technical, social) the latter raises. As a result we propose a practical method to analyze advanced pricing and rationing policies differentiated for user categories, which enables us to compare alternative operative solutions with an upper bound on social welfare based on a solid theoretical background.     

A general bilevel linear programming formulation of the network design problem

A formulation of the network design problem as a bilevel linear program is presented which admits both the convex and concave investment functions. It also allows a more general representation of travel cost functions than a previous formulation by LeBlanc and Boyce (1986).     

Cognitive cost in route choice with real-time information: An exploratory analysis

Real-time traffic information is increasingly available to support route choice decisions by reducing the travel time uncertainty. However it is likely that a traveler cannot assess all available information on all alternative routes due to time constraints and limited cognitive capacity. This paper presents a model that is consistent with a general network topology and can potentially be estimated based on revealed preference data. It explicitly takes into account the information acquisition and the subsequent path choice. The decision to acquire information is assumed to be based on the cognitive cost involved in the search and the expected benefit defined as the expected increase in utility after the search. A latent class model is proposed, where the decision to search or not to search and the depth of the search are latent and only the final path choices are observed. A synthetic data set is used for the purpose of validation and ease of illustration. The data are generated from the postulated cognitive-cost model, and estimation results show that the true values of the parameters can be recovered with enough variability in the data. Two other models with simplifying assumptions of no information and full information are also estimated with the same set of data with significantly biased path choice utility parameters. Prediction results show that a smaller cognitive cost encourages information search on risky and fast routes and thus higher shares on those routes. As a result, the expected average travel time decreases and the variability increases. The no-information and full-information models are extreme cases of the more general cognitive-cost model in some cases, but not generally so, and thus the increasing ease of information acquisition does not necessarily warrant a full-information model.     

A scalable non-myopic dynamic dial-a-ride and pricing problem for competitive on-demand mobility systems

We propose a competitive on-demand mobility model using a multi-server queue system under infinite-horizon look-ahead. The proposed approach includes a novel dynamic optimization algorithm which employs a Markov decision process (MDP) and provides opportunities to revolutionize conventional transit services that are plagued by high cost, low ridership, and general inefficiency, particularly in disadvantaged communities and low-income areas. We use this model to study the implications it has for such services and investigate whether it has a distinct cost advantage and operational improvement. We develop a dynamic pricing scheme that utilizes a balking rule that incorporates socially efficient level and the revenue-maximizing price, and an equilibrium-joining threshold obtained by imposing a toll on the customers who join the system. Results of numerical simulations based on actual New York City taxicab data indicate that a competitive on-demand mobility system supported by the proposed model increases the social welfare by up to 37% on average compared to the single-server queuing system. The study offers a novel design scheme and supporting tools for more effective budget/resource allocation, planning, and operation management of flexible transit systems.     

基于AMESim的插电式并联混合动力汽车能量管理策略仿真分析

为了缩短混合动力汽车开发时间,减少开发成本,本文以插电式并联混合动力汽车为研究对象,针对设计指标进行动力系统参数匹配以及使用AMESim软件搭建了整车模型,然后设计了基于门限值的能量管理策略并使用AMESim软件中的Signal,Control库进行搭建。之后对已搭建完成的车辆进行动力性经济性仿真分析,其中经济性分析是在NEDC工况下进行的,验证了本文所搭建策略和整车模型的正确性和可行性。     

Revenue allocation methods for integrated transit systems

Revenue allocation in the context of an integrated transit system involves the splitting of joint revenues derived from passengers taking system trips, that is, trips that involve a transfer between properties of two or more of the participants in an integrated system. The general nature of the revenue allocation problem is first reviewed. Next, a framework for constructing and assessing revenue allocation models is developed. A macroprocess model is described; categories of variables that merit consideration for inclusion in revenue/cost allocation formulas are discussed; and criteria for evaluation of models are examined. After doing this, five general approaches to the problem of allocating joint revenues are discussed and evaluated. Finally, a general revenue-sharing model based on ridership is developed. The model is then used to examine several relevant issues in pricing system trips and fare collection.     

Integrated transit services for minimum cost operation considering heterogeneous demand

Abstract

In large metropolitan areas, public transit is a major mode choice of commuters for their daily travel, which has an important role in relieving congestion on transportation corridors. The purpose of this study is to develop a model which optimizes service patterns (SPs) and frequencies that yield minimum cost transit operation. Considering a general transit route with given stops and origin-destination demand, the proposed model consists of an objective total cost function and a set of constraints to ensure frequency conservation and sufficient capacity subject to operable fleet size. A numerical example is provided to demonstrate the effectiveness of the developed model, in which the demand and facility data of a rail transit route were given. Results show that the proposed model can be applied to optimize integrated SPs and headways that significantly reduce the total cost, while the resulting performance indicators are generated.     

Using nonparametrics to specify a model to measure the value of travel time

Using a range of nonparametric methods, the paper examines the specification of a model to evaluate the willingness-to-pay (WTP) for travel time changes from binomial choice data from a simple time–cost trading experiment. The analysis favours a model with random WTP as the only source of randomness over a model with fixed WTP which is linear in time and cost and has an additive random error term. Results further indicate that the distribution of log WTP can be described as a sum of a linear index fixing the location of the log WTP distribution and an independent random variable representing unobserved heterogeneity. This formulation is useful for parametric modelling. The index indicates that the WTP varies systematically with income and other individual characteristics. The WTP varies also with the time difference presented in the experiment which is in contradiction of standard utility theory.     

The Measurement of Efficiency of UK Airports,Using a Stochastic Latent Class Frontier Model

Abstract

In this paper, the latent stochastic frontier model is used to estimate the technical efficiency of UK airports. These airports are ranked according to their technical efficiency for the period 2000–06 and the airports are disentangled and segmented by the cost frontier model, which leads us to advise the implementation of policies by segments. Economic implications arising from the study are also considered.     

Technical change and productivity growth in airports: A case study

This paper analyses the rate of technical progress at Portuguese airports between 1990 and 2000, using a stochastic cost frontier method. A translog frontier model is used and the maximum likelihood estimation technique is employed to estimate the empirical model. We estimate the efficient scores and disentangle the rate of technical progress into three components: pure technical progress, non-neutral technical progress and scale-augmenting technical progress. The results show that the average score is 88%, denoting a relatively high degree of waste, despite the fact that technical change contributed to a reduction in costs.     

Developing green fleet management strategies: Repair/retrofit/replacement decisions under environmental regulation

The considerable cost of maintaining large fleets has generated interest in cost minimization strategies. With many related decisions, numerous constraints, and significant sources of uncertainty (e.g. vehicle breakdowns), fleet managers face complex dynamic optimization problems. Existing methodologies frequently make simplifying assumptions or fail to converge quickly for large problems. This paper presents an approximate dynamic programming approach for making vehicle purchase, resale, and retrofit decisions in a fleet setting with stochastic vehicle breakdowns. Value iteration is informed by dual variables from linear programs, as well as other bounds on vehicle shadow prices. Sample problems are based on a government fleet seeking to comply with emissions regulation. The model predicts the expected cost of compliance, the rules the fleet manager will use in deciding how to comply, and the regulation’s impact on the value of vehicles in the fleet. Stricter regulation lowers the value of some vehicle categories while raising the value of others. Such insights can help guide regulators, as well as the fleet managers they oversee. The methodologies developed could be applied more broadly to general multi-asset replacement problems, many of which have similar structures.     

The influence of travel time variability on the value of time

Current benefits from travel time savings have only been related to the benefits from reducing mean travel time. Some previous attempts of including variability in the generalised cost function have mainly assumed commuters with fixed arrival time. This paper presents a comprehensive framework for valuing travel time variability that allows for any journey purpose and arrival time constraint. The proposed model is based on the expected utility approach and the mean-standard deviation approach. Stated Preference methods are considered the best technique for providing the data for calibrating the models. The values of time derived from the models are highly influenced by the value of travel time variability and it strongly depends on the probability distribution function travellers are faced with.