Evaluating the Rail-Based Multimodal Freight Transportation after HSR Entry in Yangtze River Delta Economics Zone

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Introduction
In the past five years, China invested more than 80 billion yuan each year in the construction of high-speed rail (HSR). By the end of 2019, the total revenue length of HSR in China had reached 35,000 km and ranked first place in the world. HSR commanded a share of 64.4% in the total rail passengers by the end of 2019. At the same time, the development of HSR and the shift away from traditional passenger rail services have released the capacity of conventional rail infrastructures to support freight transport including multimodal freight transport. In China State Council's ree-Year Yangtze River Delta has already reached 5,000 kilometers, and four main HSR lines go through in this region and join in Shanghai, as shown in Figure 1. e Jinghu, Hukun, and Yanjiang HSR lines all start from Shanghai and link through several main cities in the Yangtze River Delta. Meanwhile, the Yanhai HSR is also being designed to link the main coastal cities of the Yangtze River Delta. As a result, the HSR had already been the main choice for rail travelers, and the released capacity of conventional rail infrastructures is transferred for the use of freight transportation in the Yangtze River Delta. At the same time, with the implementation of e Action Plan since 2018, rail-based multimodal transport has been quickly developed. Such revolutionary changes have not been documented and examined by the existing studies. Even in other countries where the HSR network has been developed, the impact of HSR on freight transportation, particularly its impact on rail-based multimodal transportation, has been largely ignored.
erefore, this study aims to document the current status of the development of rail-based multimodal freight transportation in the Yangtze River Delta due to the capacity freed up by the launch of HSR. In addition, we construct an AHP-based performance index comprising qualitative and quantitative indicators to evaluate the development of the rail-based multimodal freight transportation network including rail-rail, rail-water, and rail-road.
is study will inform relevant stakeholders, particularly the policymakers to recognize the current performance of the multimodal transportation in each province in the Yangtze River Delta, which will assist them to design a long-term transport master plan for a multimodal freight transport network in the Yangtze Delta as HSR construction continues in this region.

Background
e study targets the multimodal freight transport network among three provinces (Zhejiang, Jiangsu, and Anhui) and one municipality (Shanghai) in e Yangtze River Delta region. Based on the freight transport data in 2018 and 2019, the study chooses 59 rail stations (the chosen stations accounting for 80% of the whole rail transport traffic in 2018 in Yangtze River Delta) as the key nodes to form the evaluation network. e evaluated network includes not only cities from the arterial rail lines but also main cities from the branch lines. is sampling network thus contains 31 cities with HSR stations and more than 200 railway links as shown in Figure 2. In order to capture the booming water-based transportation performance [1] considering sustainable requirements as mentioned in Tan and He [2] and He et al. [3], eight ports with railway freight stations are also evaluated in this research.
All the 59 evaluated rail stations are located in the conventional rail freight network within the Yangtze River Delta. As every city in the Yangtze River Delta has already been linked by the HSR, all the evaluated freight stations are likely affected by the HSR development. To have a closer look at the effects of the HSR development, this study separates the 59 stations into 29 arterial stations and 30 branch stations. All the 29 arterial stations are located along the national eight vertical and eight horizontal HSR lines. e distribution of the 59 stations can be found in Table 1. e freight and container traffic at these stations in 2019 for the three provinces and Shanghai is reported in Table 2. It can be seen that arterial stations recorded the largest increase, particularly in Jiangsu province. However, the freight traffic at the stations in Shanghai slightly dropped from 2018 to 2019. Freight traffic at the arterial stations in Anhui also recorded a slight decrease in 2019. e freight multimodal network in the Yangtze River Delta is well developed. For the rail-road transport, Anhui outperformed the other three provinces because of the highly developed conventional railway freight network (see Figure 3). Meanwhile, rail-road is rarely used to distribute freight and containers in Shanghai.
Figures 4 and 5 present the market shares of water-based multimodal transportation modes at eight ports in the Yangtze River Delta area in 2019. It can be seen that railwater mode accounted for a very small share in all the provinces except for Anhui. e region has very developed water transportation systems, and most of these ports mainly focus on water-water freight transportation. For container transportation, road-based transport is still the first choice by customers for most areas in the Yangtze River Delta.
ere is much room to promote the rail-water model in this region.

Literature Review
China's first HSR was launched between Beijing and Tianjin. e first long-haul HSR was the HSR put into use in 2009. A decade later, China has become a world leader in HSR construction [4]. e total length of China's HSR will reach 38,000 km by 2025 and 45,000 by 2030. e impact of HSR on various aspects of the economy has been well studied (for a good survey, see [5]). After more than ten years' construction, the entry of HSR has made a significant impact on the regional development in China. Chen and Haynes [6] propose a conceptual framework to assess the impact of HSR on regional economic disparity. ey confirm that the regional economic disparity has been decreased since the development of HSR in China. Zhang et al. [7] studied the impacts of HSR development on regional equity in China and found that the launch of HSR is positively associated with provincial equity. Long et al. [8] report that the entry of HSR accelerates urban expansion and benefit more for the underdeveloped central and western cities in China. Liang et al. [9] hold a similar view. However, Li et al. [10] found that positive impacts are shown to be greater for metropolis than small cities, which implies that HSR also contributes significantly to the economic development of the wealthy eastern region [11] including the Yangtze River Delta Region and Pearl River Delta Region [12]. Despite these positive impacts, Sun and Mansury [13] pointed out that HSR may also contribute to the widening gap between developed and underdeveloped regions. Overall, it seems that the benefits brought about by HSR are significant and unambiguous. However, up to today, there is a lack of systematic and comprehensive approaches to quantify the impacts of HSR on the economy as a whole or one particular industry or sector.
International experiences have proven that the HSR development has direct and indirect effects on regional development and creates opportunities to reconstruct the urban system, in both spatial and economic terms [14]. Cascetta et al. [15] evaluated the impact of HSR on economic growth, transport accessibility, and regional quality in Italy ten years after the HSR operation and found significant links between these variables. With the development of the HSR network, the impacts of HSR are also widely received in China. Chen [16] assessed the economic and environmental impact of HSR development from the regional development perspective with proofs where the effects of HSR on the increase of the land value, housing value, and tourism demand are real and significant. From the tourism perspective, Weng et al. [17] revealed that the HSR has become the main choice for short-and medium-range travel distances for tourists. e domestic tourism market improvement due to the presence of HSR is particularly for small-and mediumsized cities. Chen and Haynes [18] and Yang and Li [19] both found that the development of HSR can attract more international tourism and boost inbound tourism.
Furthermore, the development of HSR poses a big challenge to traditional transport markets especially the air transport sector [5,20]. Zhang et al. [21] and Zhang et al. [22] demonstrated that the negative impacts on air transport are strong with the entry of HSR, particularly for major airline routes. e development of HSR has forced airline companies to reduce their prices and improve services, particularly punctuality to retain existing and attract new passengers [23,24]. Its threat to low-cost carriers is more prominent due to the close substitution of the two [25]. Meanwhile, the opening of HSR has gradually reshaped the regional freight transport structure. With the entry of HSR, rail-based freight transport becomes more competitive in the regional multimodal transport systems. It is acknowledged that the HSR-based freight transport system is more efficient and cost-effective [26]. However, the development of the HSR-based freight transport network requires a significant investment which is not an easy decision for many governments [27]. erefore, the conventional railway infrastructure continues to play an important role in rail-based freight transportation. Li et al. [28] indicate that there has been a significant reduction of conventional train services because of the entry of HSR, which makes room for freight Scientific Programming transportation using the traditional train lines. is represents a spillover effect of HSR development. However, in the existing literature on HSR impacts, little has been said about the promotion effect of HSR on the development of railbased freight transportation. is research aims to fill this literature gap by designing a comprehensive performance index to measure the development of the rail-based freight multimodal transport system, including rail-rail, rail-road, and rail-water in the Yangtze River Delta.

Methodology
is study applies a three-level AHP structure and constructs a comprehensive index to evaluate the development of a rail-based multimodal freight transport network. is AHP-based performance index contains 14 quantitative and 8 qualitative indicators, covering the rail-based infrastructures, multimodal transport capability, freight transport performance, and transport sustainability. e index has three levels with the first level including regional distribution network, multimodal transport capacity, and transport operational performance sustainability as shown in Table 3.   All the indicators are selected by the brainstorming meeting with experts experienced with rail-based transport from the Yangtze River Delta region. Meanwhile, a questionnaire was designed to collect transport experts' opinions on the qualitative indicators. e survey includes two parts: the first part is to compare the significance between indicators as shown in Table 4; the second part is to score the qualitative indicators (C11, C12, C13, C21, C22, D31) based on the questions in Table 5. e questionnaires were sent to more than 200 logistics companies that had multimodal freight transport experience and 172 valid questionnaires were collected. Meanwhile, all the quantitative indicators are scored based on real-life freight data. e single-level ordering method is used to score the three-level index system. To validate the index system's consistency, this study uses the ANC (Asymptotic Normalization Coefficient) method to calculate the maximal eigenvalue of the AHP judgment matrix B and its corresponding eigenvector. e judgment matrix B is defined as where b ij represents the importance of index i to index j at a certain level. e matrix B is obtained by the expert scoring method. Each column of the matrix is then normalized, with the formula as follows: e matrix after column normalization is added by row e eigenvector can be expressed as W � [W 1 , W 2 , . . . , W N ] T , so that the maximal eigenvalue λ max of the judgment matrix can be calculated through e solution of the maximal eigenvalue λ max is the basis of the consistency verification, which is carried out by the following formulas:      Scientific Programming CI represents the consistency verification value. e value of RI depends on the dimensions of the index. Dimensions 1 to 4 are 0, 0, 0.58, and 0.90, respectively.
If the verification results of CI meet the above conditions, the index system meets the consistency requirements. e eigenvector W � [W 1 , W 2 , . . . , W N ] T can be considered as the evaluation index weight that represents the importance ranking of the index to its superior index. e description of the significance comparison between indicators can be found in Table 4. Using equations (1)-(4), the index scores for qualitative indicators (C11, C12, C13, C21, C22, D31) can be calculated. For quantitative indicators, calculations are made using real-life data. e details of the raw data for every indicator can be found in Table 5. It is necessary to normalize the data of different dimensions and then calculate the corresponding scores. By doing so, we can obtain different scores of the third-level indicators. e second-level indicators' scores are obtained by the weighted sum of the corresponding third-level indicators' weights and scores. e first-level indicators' scores are obtained by the weighted sum of the second-level indicators' weights and scores. e scores of different indicators can be applied to analyze the development of multimodal transportation within the evaluated network. Compared with 2015, the rail freight traffic experienced a drop in 2019, probably because, in recent years, China has taken actions to restrict the consumption of coals to protect the environment, which has decreased the demand for bulk transportation. It is also noticed that the drop took place at branch stations. In contrast, the arterial stations recorded a substantial increase in traffic, largely due to the capacity release as a result of the HSR development. It seems that freight traffic has become more concentrated in the arterial stations. Our interviewers note that with the development of the HSR network, more passengers (80% of the total railway Table 4: e description of the comparison significance between indicators is introduced as Mu and Pereyra-Rojas [29].

e Impact of HSR on Railway Transportation in Yangtze
Significance (i) Description 9 Compared with j, indicator i is extremely important 7 Compared with j, indicator i is very strongly more important 5 Compared with j, indicator i is strongly more important 3 Compared with j, indicator i is moderately more important 1 Compared with j, indicator i is equally important 2, 4, 6, 8 e intermediate values between the two adjacent judgments, used when a compromise is needed  As a result, the conventional railway infrastructures were released for handling freight, which can explain the rise in freight traffic at arterial stations. e rise in container traffic is not surprising as, in recent years, incentive policies such as those noted in e Action Plan have been implemented to promote containerization transportation. Figures 8 and 9 further report the changes in freight and container traffic in these two years for Shanghai and three provinces. e same pattern can be observed for each province, suggesting that HSR has promoted the freight traffic at arterial stations. With the impacts of HSR development, the freight transport for arterial stations has been shown with a better opportunity to lead the rail-based freight transport. e results guide policymakers on the one hand focus on optimizing the conventional rail infrastructures to Table 5: e description of original data for every indicator.

Indicators
Original data description Public rail stations and railway links (A11) e number of railway stations and links for public freight transport in the evaluated network Exclusive railway (A12) e number of railway links of transporting freight for exclusive companies in the evaluated network Port shoreline (A13) e length of the berthing line [30] for the evaluated ports   8 Scientific Programming support rail-based freight transport for arterial stations and, on the other hand, release more incentives for promoting rail-based traffic for branch stations.

e Current Status of the Rail-Based Multimodal Transport in Yangtze River Delta.
e evaluation index weights based on Section 4 are shown in Table 6. e index scores for each indicator and province are reported in Table 7. Zhejiang leads other provinces with the highest overall score, 92.36, followed by Jiangsu, 89.97; Anhui, 88.32; and Shanghai, 88.05. e scores of the first-level indicators are shown in Figure 10. It can be seen that Zhejiang scores the highest in multimodal transport capacity, transport operational performance, and sustainability, while Anhui has the highest score in the regional distribution network. e evaluation results of the regional distribution network are shown in Figure 11. Shanghai's transport infrastructures are relatively strong, especially in terms of highway density, port shoreline, and port throughput as shown in Table 7. However, their weights are small based on expert scoring which makes Shanghai lag behind in the railbased infrastructure and operations. In contrast, Anhui is more developed in exclusive railway and highway freight volume, public rail station, and exclusive railway links, whose weights are relatively higher. As a result, Anhui has the best multimodal transport operations. Meanwhile, Zhejiang has a balanced development in public rail freight volume, port throughput, and its regional distribution network. In terms of multimodal transport capability in Figure 12, Zhejiang and Jiangsu have a better overall performance, and they are well developed in rail-water transport. For Shanghai, water-based transport including waterwater transport and road-water transport has developed more prominently, while Anhui has the best road-rail transport performance. e evaluation results of the transport operational performance can be found in Figure 13. e research has investigated the main multimodal transport market in the Yangtze River Delta. e results show that Zhejiang received relatively higher scores for transport performance and technical improvement. Shanghai and Jiangsu do not lag too far behind in these two indicators because the development of technical improvement and transport performance is well developed in the Yangtze River Delta region. ere was no railway freight accident recorded in 2019 and all provinces received 100 points. However, the technical improvement and transport performance of Anhui received relatively lower points, implying that there is room for improvement for the indicators of transport delay, transfer efficiency, revenue, informatization, and availability as shown in Table 7.
In terms of sustainability, the scores can be seen in Figure 14. Shanghai is highly restricted by the strong waterbased transport and market, which have affected the Scientific Programming development of railway freight transport, so the score is significantly lower. Meanwhile, the governmental policies seem too weak to promote the transformation of Shanghai's transport structure. In contrast, the governmental policies in Zhejiang are more active to stimulate rail-based transport.
Considering the environmental impacts, Jiangsu has the best performance with less road-based transport and more green modals. Meanwhile, Anhui is developing rapidly in the railbased multimodal transport based on the well-developed rail infrastructures.

Conclusions
is research proposes a method to evaluate the current status of the rail-based multimodal freight transport system in the Yangtze River Delta economics zone and reveals the impacts of HSR development on rail-based freight transport. Here are some major findings: (1) e rail-based freight transport gradually plays a more important role in the multimodal transport in the Yangtze River Delta region due to the capacity releases of the conventional railway with the growth of the HSR network. e development of HSR has a positive impact on the rail-based freight and container traffic especially for the railway stations along the eight vertical and eight horizontal arteries. erefore, the policymaker should pay more attention to optimizing the conventional railway schedules for arterial stations to improve freight transport.
(2) e original transport market and modals still determine the multimodal transport structures. Zhejiang has led the rail freight transport while Shanghai mainly leads the waterway freight transportation. Meanwhile, the road-rail transport in Anhui and water-rail transportation in Jiangsu are also well developing within the Yangtze River Delta region. us, the policymaker should design different incentives based on the original transport modals in each region to promote rail-based freight transport.
(3) Overall, the designed comprehensive index is shown to be efficient to evaluate multimodal transport, especially for rail-based freight transport. Currently, rail-based freight transport is still not competitive compared with road-based and water-based transport in the Yangtze River Delta region. However, the timely updates of this evaluation are important for the government to trace the development of rail-  based multimodal transport and release more efficient policies.
Besides, the proposed evaluation method is also available for evaluating the development of rail-based multimodal transport for individual cities and logistic centers, which is also inspiring for developing rail-based freight transport in different levels.

Data Availability
No data were used to support this study.

Conflicts of Interest
e authors declare that they have no conflicts of interest.