Unequal regional impacts of high speed rail on the tourism industry: a simulation analysis of the effects of Kyushu <Emphasis Type="Italic">Shinkansen</Emphasis>

Many high speed rail (HSR) routes are under construction in various cities of the world. Although tourism is one of the industries affected by HSR, not much is known about its effects on the same. This paper studies the impact of Kyushu’s HSR (Shinkansen) on tourism using computable general equilibrium modeling in the context of regional economies and transportation. The results show that the HSR has unequal effects on tourism among prefectures. The presence of these inequalities depends on whether the prefecture is a served by HSR, whether it is a terminal or an intermediate HSR station, and its current popularity with the tourists. Despite these inequalities, the economies of all the prefectures are benefited by the HSR owing to general equilibrium effects.     

Carbon,water, land and material footprints of China’s high-speed railway construction

The rapid development of China’s high-speed rail (HSR) has caused great concern worldwide, and its various environmental impacts is also a main topic. As such, a comprehensive assessment of these environmental impacts is significant for future HSR construction. In this study, a hybrid input-output life cycle method is applied to evaluate multi-footprints of Beijing-Tianjin intercity HSR, namely, carbon, water, land, and material footprints. Results show that the total carbon, water, land, and material footprints of the entire Beijing-Tianjin HSR construction are 3669.0 kt CO₂, 54954.7 kt fresh water, 45830.6 ha land, and 8474.2 kt original materials. In the construction stage, bridge and rail systems have the largest environmental impacts, totally contributing above 74% of the four footprints. The indirect footprints from upstream material production have the dominant effects, accounting over 89% in the four footprints. Metal smelting and rolling, transportation equipment manufacturing, and non-metallic mineral products are high-water-consuming and high-emission sectors in raw material production. Improving industrial production efficiency, optimizing line layout, optimizing production distribution and construction process management are key mitigation ways for multi-footprints.     

A Review of Ex-Post Evidence for Mode Substitution and Induced Demand Following the Introduction of High-Speed Rail

ABSTRACT

To date, relatively little is known about the nature of the demand for high-speed rail (HSR) soon after inauguration of the services, despite close to 50-year experience of HSR operation and 17 166?km of HSR network around the world. This is a real lacuna given the scale of HSR construction around the world, the amount of resources committed to it, the desired accessibility, economic and environmental effects associated with HSR development and the relatively poor track record of forecasting demand for HSR services. Focusing on mode substitution and induced demand effects, this review aims to fill the gap in knowledge about the ex-post demand for HSR services in order to facilitate a learning process for the planning of the future HSR network. Although there is not much evidence on the demand for HSR services and existing evidence is largely influenced by route-specific characteristics, a methodological limitation that must be acknowledged, the evidence presented allows a better characterisation of HSR as a mode of transport. The review shows that the demand for HSR a few years after inauguration is about 10–20% induced demand and the rest is attributed to mode substitution. In terms of mode substitution, in most cases the majority of HSR passengers have used the conventional rail before. Substitution from aircraft, car and coach is generally more modest.     

Airlines’ reaction to high-speed rail entries: Empirical study of the Northeast Asian market

We investigate the impact of the commencement of high-speed rail (HSR) services on airlines’ domestic available seats on affected routes in China, Japan, and South Korea. The study is based on a dataset covering the 1994–2012 period. We use the propensity score matching method to pair HSR affected routes with routes without HSR services. The difference-in-difference approach is used to estimate the impact of HSR entry. We find that HSR entries may, on average, lead to a more significant drop in airlines’ seat capacity in China than in Japan and Korea given similar HSR service speed. In China, HSR services with a maximum speed about 200 km/h can produce strong negative impacts on medium-haul air routes but induce more air seat capacity on long-haul routes. HSR services with a maximum speed of 300 km/h have little extra impact on medium-haul routes but a strong negative impact on long-haul routes. Finally, although HSR has a strong negative impact in Japan’s short-haul and medium-haul air markets, little impact is observed in its long-haul markets.     

The role of access and egress in passenger overall satisfaction with high speed rail

Passenger satisfaction is critical to ridership growth of high speed rail (HSR). Each HSR trip includes at least four segments: access to HSR stations, waiting, line-haul, and egress from HSR stations. Satisfaction with any segment influences the HSR passenger experience. Previous studies often focus on passenger satisfaction with the line-haul segment, but overlook the effects of all four segments on overall HSR satisfaction, especially access and egress. Using a path analysis on the data collected from the Shanghai-Nanjing HSR corridor in 2016, this study explores the influence of access and egress segments on overall HSR satisfaction and the correlates of satisfaction with HSR access and egress segments. We find that HSR line-haul satisfaction dominates overall HSR satisfaction; HSR access and egress satisfaction together have an equivalent effect. Travel time and route familiarity are important to both access and egress satisfaction. Mode choice affects satisfaction with HSR egress, with egress by car carrying the largest utility of egress satisfaction, followed by rail transit, taxi, and then bus. Thus, to improve HSR experience, traveler information service and the integration of HSR with urban transportation system are critical.

     

An optimization model of energy and transportation systems: Assessing the high-speed rail impacts in the United States

This paper presents a long-term investment planning model that co-optimizes infrastructure investments and operations across transportation and electric infrastructure systems for meeting the energy and transportation needs in the United States. The developed passenger transportation model is integrated within the modeling framework of a National Long-term Energy and Transportation Planning (NETPLAN) software, and the model is applied to investigate the impact of high-speed rail (HSR) investments on interstate passenger transportation portfolio, fuel and electricity consumption, and 40-year cost and carbon dioxide (CO₂) emissions. The results show that there are feasible scenarios under which significant HSR penetration can be achieved, leading to reasonable decrease in national long-term CO₂ emissions and costs. At higher HSR penetration of approximately 30% relative to no HSR in the portfolio promises a 40-year cost savings of up to $0.63 T, gasoline and jet fuel consumption reduction of up to 34% for interstate passenger trips, CO₂ emissions reduction by about 0.8 billion short tons, and increased resilience against petroleum price shocks. Additionally, sensitivity studies with respect to light-duty vehicle mode share reveal that in order to realize such long-term cost and emission benefits, a change in the passenger mode choice is essential to ensure higher ridership for HSR.     

Grand challenges for high-speed rail environmental assessment in the United States

The comprehensiveness of environmental assessments of future long-distance travel that include high-speed rail (HSR) are constrained by several methodological, institutional, and knowledge gaps that must and can be addressed. These gaps preclude a robust understanding of the changes in environmental, human health, resource, and climate change impacts that result from the implementation of HSR in the United States. The gaps are also inimical to an understanding of how HSR can be positioned for 21st century sustainability goals. Through a synthesis of environmental studies, the gaps are grouped into five overarching grand challenges. They include a spatial incompatibility between HSR and other long-distance modes that is often ignored, an environmental review process that obviates modal alternatives, siloed interest in particular environmental impacts, a dearth of data on future vehicle and energy sources, and a poor understanding of secondary impacts, particularly in land use. Recommendations are developed for institutional investment in multimodal research, knowledge and method building around several topics. Ultimately, the environmental assessment of HSR should be integrated in assessments that seek to understand the complementary and competitive configurations of transportation services, as well as future accessibility.     

Autonomous cars: The tension between occupant experience and intersection capacity

Systems that enable high levels of vehicle-automation are now beginning to enter the commercial marketplace. Road vehicles capable of operating independently of real-time human control under an increasing set of circumstances will likely become more widely available in the near future. Such vehicles are expected to bring a variety of benefits. Two such anticipated advantages (relative to human-driver vehicle control) are said to be increased road network capacity and the freeing up of the driver-occupant’s time to engage in their choice of leisurely or economically-productive (non-driving) tasks.In this study we investigate the implications for intersection capacity and level-of-service of providing occupants of automated (without real-time human control), autonomously-operating (without vehicle-to-X communication) cars with ride quality that is equivalent (in terms of maximum rates of longitudinal and lateral acceleration) to two types of rail systems: [urban] light rail transit and [inter-urban] high-speed rail. The literature suggests that car passengers start experiencing discomfort at lower rates of acceleration than car drivers; it is therefore plausible that occupants of an autonomously-operating vehicle may wish to instruct their vehicle to maneuver in a way that provides them greater ride comfort than if the vehicle-control algorithm simply mimicked human-driving-operation.On the basis of traffic microsimulation analysis, we found that restricting the dynamics of autonomous cars to the acceleration/deceleration characteristics of both rail systems leads to reductions in a signalized intersection’s vehicle-processing capacity and increases in delay. The impacts were found to be larger when constraining the autonomous cars’ dynamics to the more-restrictive acceleration/deceleration profile of high-speed rail. The scenarios we analyzed must be viewed as boundary conditions, because autonomous cars’ dynamics were by definition never allowed to exceed the acceleration/deceleration constraints of the rail systems. Appropriate evidence regarding motorists’ preferences does not exist at present; establishing these preferences is an important item for the future research agenda.This paper concludes with a brief discussion of research needs to advance this line of inquiry.     

A hierarchical line planning approach for a large-scale high speed rail network: The China case

Planning a set of train lines in a large-scale high speed rail (HSR) network is typically influenced by issues of longer travel distance, high transport demand, track capacity constraints, and a non-periodic timetable. In this paper, we describe an integrated hierarchical approach to determine line plans by defining the stations and trains according to two classes. Based on a bi-level programming model, heuristics are developed for two consecutive stages corresponding to each classification. The approach determines day-period based train line frequencies as well as a combination of various stopping patterns for a mix of fast trunk line services between major stations and a variety of slower body lines that offer service to intermediate stations, so as to satisfy the predicted passenger transport demand. Efficiencies of the line plans described herein concern passenger travel times, train capacity occupancy, and the number of transfers. Moreover, our heuristics allow for combining many additional conflicting demand–supply factors to design a line plan with predominantly cost-oriented and/or customer-oriented objectives. A range of scenarios are developed to generate three line plans for a real-world example of the HSR network in China using a decision support system. The performance of potential train schedules is evaluated to further examine the feasibility of the obtained line plans through graphical timetables.     

High-speed rail and air transport competition and cooperation: A vertical differentiation approach

This paper considers vertical differentiation between air transport and high-speed rail (HSR) with different ranges of travel distance to analyze the air-HSR competition effects on fares, traffic volumes and welfare, as well as the conditions under which air-HSR cooperation is welfare-enhancing. The analysis is conducted in a hub-and-spoke network with a network carrier, an HSR operator, and a spoke airline, taking into account potential hub airport capacity constraint. We find that air-HSR competition in the connecting market may result in the network airline charging an excessively high price in the HSR-inaccessible market. This effect is present even when the HSR-inaccessible route is a duopoly-airline market. On the other hand, air-HSR cooperation increases fares in the connecting market, and an improvement in rail speed or air-HSR connecting time reduces airfare on the routes where HSR and the airline compete. When the airline cannot serve all the markets due to limited hub airport capacity, it would withdraw from the market in which it has less competitive advantage over HSR. Finally, air-HSR cooperation is more likely to be welfare-improving when the hub airport is capacity constrained, and when either air transport or HSR exhibits strong economies of traffic density.     

Tracks to Change or Mixed Signals? A Review of the Anglo-Saxon Literature on the Economic and Spatial Impacts of High-Speed Rail

ABSTRACT

The economic development and spatial impacts of high-speed rail (HSR) are quite varied and mixed. Studies of economic impacts of HSR fall into two categories: predictions and empirical observations. We first review studies that examine predicted HSR impacts related to job growth, real estate development, and other economic effects. These are followed by studies examining observed effects of realized projects using the same categories. Several patterns emerge. Predictive studies are largely optimistic of the rail's positive effects. On the other hand, observational studies tend to identify both benefits and shortfalls. Yet, neither of these patterns are universal and depend on a number of other factors. One consistent pattern is that gains associated with HSR appear to be stronger in first-tier cities. Nevertheless, certain small towns and second-tier cities have undergone noteworthy transformations as a result of HSR, especially where the public sector has actively pursued new investment.     

Forecasting high-speed rail ridership using a simultaneous modeling approach

Abstract

The newly launched, June 2009, US High-Speed Intercity Passenger Rail Program has rekindled a renewed interest in forecasting high-speed rail (HSR) ridership. The first step to the concerted effort by the federal, state, rail, and other related agencies to develop a nationwide HSR network is the development of credible approaches to forecast the ridership. This article presents a nested logit/simultaneous choice model to improve the demand forecast in the context of intercity travel. In addition to incorporating the interrelationship between trip generation and mode choice decisions, the simultaneous model also provides a platform for the same utility function flowing between both the decision-making processes. Using American Travel Survey data, supplemented by various mode parameters, the proposed model improves the forecast accuracy and confirms the significant impact of travel costs on both mode choice and trip generation. Furthermore, the cross elasticity of mode choice and trip generation related to travel costs and other modal characteristics may shed some light on transportation policies in the area of intercity travel, especially in anticipation of HSR development.     

Forecasting demand for high speed rail

It is sometimes argued that standard state-of-practice logit-based models cannot forecast the demand for substantially reduced travel times, for instance due to High Speed Rail (HSR). The present paper investigates this issue by reviewing the literature on travel time elasticities for long distance rail travel and comparing these with elasticities observed when new HSR lines have opened. This paper also validates the Swedish long distance model, Sampers, and its forecast demand for a proposed new HSR, using aggregate data revealing how the air–rail modal split varies with the difference in generalized travel time between rail and air. The Sampers long distance model is also compared to a newly developed model applying Box–Cox transformations. The paper contributes to the empirical literature on long distance travel, long distance elasticities and HSR passenger demand forecasts. Results indicate that the Sampers model is indeed able to predict the demand for HSR reasonably well. The new non-linear model has even better model fit and also slightly higher elasticities.     

A data perception model for the safe operation of high-speed rail in rainstorms

In order to solve the safety operation problems of High-speed rail (HSR) in different areas and different sections under the rainstorm condition, an early warning process for the rainstorm disaster is designed. Furthermore, in order to control the operation risk, a HSR operation program with different rainstorm degrees is given out based on the analysis of rainstorm warning mechanism and rainstorm warning threshold in this paper. In addition, considering the reality that natural conditions vary greatly and the rainfall is very uneven, a data perception model of rainstorm (DPM) is proposed with correction coefficients for solving the calculation problem of precipitation for rainstorm warning. The DPM mainly adopts Paulhus’s empirical equation and uses the linear function to improve it for calculating the precipitation, which is able to calculate the hourly precipitation in different regional environments, and also effectively evaluate the rainstorm warning level of high-speed rail in this period. It can calculate and monitor the process by big data and MATLAB. The result of case analysis shows that the DPM has good practical value for solving the safety operation problem of HSR in different areas under rainstorm environment.     

Effects of high-speed rail and airline cooperation under hub airport capacity constraint

This paper analyzes the effects of cooperation between a hub-and-spoke airline and a high-speed rail (HSR) operator when the hub airport may be capacity-constrained. We find that such cooperation reduces traffic in markets where prior modal competition occurs, but may increase traffic in other markets of the network. The cooperation improves welfare, independent of whether or not the hub capacity is constrained, as long as the modal substitutability in the overlapping markets is low. However, if the modal substitutability is high, then hub capacity plays an important role in assessing the welfare impact: If the hub airports are significantly capacity-constrained, the cooperation improves welfare; otherwise, it is likely welfare reducing. Through simulations we further study the welfare effects of modal asymmetries in the demands and costs, heterogeneous passenger types, and economies of traffic density. Our analysis shows that the economies of traffic density alone cannot justify airline–HSR cooperation.     

A three-phase QFD-based framework for identifying key passenger needs to improve satisfaction with the seat of high-speed rail in China

This study aims to suggest a three-phased methodological framework based on the operational approach of quality function deployment (QFD) to improve the service quality and passenger satisfaction with China’s high-speed rail (HSR) by identifying the key passenger needs with regard the HSR seats. For the first phase, the collection of the voice of the customers/passengers (VOC), the processing of the collected VOC into need items and further into passenger needs of QFD would be explained in terms of knowledge management. For the second phase, a reference comparison-based fuzzy best–worst method is developed for determining the relative importance of passenger needs, with a particular purpose of coping with the uncertainty and ambiguity associated with qualitative assessment of respondents. For the third phase, the importance-performance analysis is performed to determine the improvement priorities for meeting passenger needs. Findings showed that Body-friendly seat structure and Reasonable layout of the seat are the two most important needs demanded by the passengers of second-class cabins, with the former being the top priority. The current study provides useful references for service operators of HSR to formulate development strategies for improving the seat comfortability, which subsequently contributes to improving HSR’s service quality and passenger satisfaction. Moreover, the proposed methodological framework for identifying the important passenger needs can be appropriately adjusted and expanded to similar transportation infrastructures and facilities.

     

Optimizing train stopping patterns and schedules for high-speed passenger rail corridors

High-speed railway (HSR) systems have been developing rapidly in China and various other countries throughout the past decade; as a result, the question of how to efficiently operate such large-scale systems is posing a new challenge to the railway industry. A high-quality train timetable should take full advantage of the system’s capacity to meet transportation demands. This paper presents a mathematical model for optimizing a train timetable for an HSR system. We propose an innovative methodology using a column-generation-based heuristic algorithm to simultaneously account for both passenger service demands and train scheduling. First, we transform a mathematical model into a simple linear programming problem using a Lagrangian relaxation method. Second, we search for the optimal solution by updating the restricted master problem (RMP) and the sub-problems in an iterative process using the column-generation-based algorithm. Finally, we consider the Beijing–Shanghai HSR line as a real-world application of the methodology; the results show that the optimization model and algorithm can improve the defined profit function by approximately 30% and increase the line capacity by approximately 27%. This methodology has the potential to improve the service level and capacity of HSR lines with no additional high-cost capital investment (e.g., the addition of new tracks, bridges and tunnels on the mainline and/or at stations).     

Short-term forecasting of high-speed rail demand: A hybrid approach combining ensemble empirical mode decomposition and gray support vector machine with real-world applications in China

Short-term forecasting of high-speed rail (HSR) passenger flow provides daily ridership estimates that account for day-to-day demand variations in the near future (e.g., next week, next month). It is one of the most critical tasks in high-speed passenger rail planning, operational decision-making and dynamic operation adjustment. An accurate short-term HSR demand prediction provides a basis for effective rail revenue management. In this paper, a hybrid short-term demand forecasting approach is developed by combining the ensemble empirical mode decomposition (EEMD) and grey support vector machine (GSVM) models. There are three steps in this hybrid forecasting approach: (i) decompose short-term passenger flow data with noises into a number of intrinsic mode functions (IMFs) and a trend term; (ii) predict each IMF using GSVM calibrated by the particle swarm optimization (PSO); (iii) reconstruct the refined IMF components to produce the final predicted daily HSR passenger flow, where the PSO is also applied to achieve the optimal refactoring combination. This innovative hybrid approach is demonstrated with three typical origin–destination pairs along the Wuhan-Guangzhou HSR in China. Mean absolute percentage errors of the EEMD-GSVM predictions using testing sets are 6.7%, 5.1% and 6.5%, respectively, which are much lower than those of two existing forecasting approaches (support vector machine and autoregressive integrated moving average). Application results indicate that the proposed hybrid forecasting approach performs well in terms of prediction accuracy and is especially suitable for short-term HSR passenger flow forecasting.     

An integrated framework for assessing service efficiency and stability of rail transit systems

A well-designed service plan efficiently utilizes its infrastructure and ensures an acceptable level of service stability with consideration of potential incidents that disturb or disrupt the rail transit services. To perform service evaluation, an integrated process combining capacity, resource usage, and system reliability is required to quantify service efficiency and stability in a consistent way. This study adopts capacity-based indices, “capacity utilization” and “expected recovery time”, as the attributes for service efficiency and stability, and develops a comprehensive evaluation framework with three corresponding modules to incorporate capacity, service plan, and system reliability and maintainability simultaneously. The capacity analysis module computes the rail transit capacities under normal and degraded operations. The reliability module classifies and fits the proper reliability and maintainability distributions to the historical interruption data. The service efficiency and stability module analyzes the results of the previous two modules and evaluates the service efficiency and stability of rail transit service plans. Empirical results show that the established evaluation framework can not only evaluate the service efficiency and stability but also identify critical sections and time slots. This tool can help rail transit operators rapidly assess their operational changes and investment strategies related to efficiency and stability so as to provide efficient and stable services to their customers.