key: cord-0736811-rfdot1ht
authors: Xu, Yuanxian; Dong, Jianjun; Ren, Rui; Yang, Kai; Chen, Zhilong
title: The impact of metro-based underground logistics system on city logistics performance under COVID-19 epidemic: A case study of Wuhan, China
date: 2021-10-30
journal: Transp Policy (Oxf)
DOI: 10.1016/j.tranpol.2021.10.020
sha: 5b8de93d0eb6143e0a7dd76047967b1dd0a5b351
doc_id: 736811
cord_uid: rfdot1ht

The global outbreak of COVID-19 has further exposed deficiencies in city logistics based on human and ground roads, such as poor emergency response capacity and high risk of infection during transportation. Metro-based underground logistics system (M-ULS) may be an innovative approach to deal with this city-level disaster due to its efficient operation, contactless and driverless characteristics. However, the market evolution process and the quantitative calculation framework of comprehensive benefits after the application of M-ULS are still unclear, which has become a problem of mutual restriction with the extensive application of M-ULS. This paper attempts to use the system dynamics method, based on the real-world simulation, to analyze the quantitative relationship between the M-ULS implementation and the city logistics performance under epidemic outbreaks. Wuhan city in China was selected as the empirical background, and five simulation scenarios were set under different implementation strategies of M-ULS in response to the epidemic. Six variables were selected to measure city logistics performance and M-ULS operation status, including demand fill-rate, unit delivery time, total deprivation cost, total transportation cost, total number of susceptible people, and utilization rate of M-ULS. The results show that M-ULS is effective in improving the performance of city logistics and responding to the epidemic. The delivery time and transportation cost have a strong impact on the market share of M-ULS. Finally, a set of incentive policies was designed to promote the adoption of M-ULS. The findings not only provide a method for evaluating the overall performance of M-ULS, but also provide a unique perspective for promoting the implementation of M-ULS and responding to the transportation challenges brought by the epidemic.

Corona Virus Disease 2019 , with its global outbreak and unpredictable duration, has exerted a huge impact on urban development and residents' life in all aspects.

The logistics industry, especially city logistics, has been seriously damaged, such as the serious lack of distribution personnel, the sharp rise in cost, increased inspection and quarantine links. Blockades and quarantines multiplied panicked residents' demands for supplies and door-to-door services, making it difficult to meet the city's basic needs with sharply reduced logistics supplies. More significantly, delivery personnel are at high risk of contracting the virus. For example, during the closure of Wuhan city, 43% of the delivery personnel experienced fever (The paper, 2020a). Therefore, in the face of the epidemic and the potential large-scale urban emergency in the future, the reform of high-manpower urban transportation mode to develop contactless smart city logistics system has been put on the urgent agenda.

Underground logistics system (ULS) has been widely recognized as an effective means for sustainable urban freight transport (Visser, 2018) . Among the different forms of ULS, metro-based underground logistics system (M-ULS) has received widespread attention due to its low construction cost and high feasibility . Independent underground operation environment and flexible freight organization process can effectively improve the efficiency of city logistics (Hu et al., 2020a) . Non-contact freight process can perfectly fit the urban transport demand under the epidemic situation, and greatly reduce the risk of virus transmission in the transportation process (Hu et al., 2020b) .

Covid-19 has become another important driver for the widespread implementation of M-ULS and ULS. Until this year, their practice had not been plain sailing, with high cost and other factors hampering the development of underground transportation (Cochrane et al., 2017) . Nor is there a reasonable benefit framework to support a reasonable assessment of the overall performance of the ULS (Chen et al., 2017) . Combined with the transport market conditions under the epidemic, quantitative analysis of the overall benefits of M-ULS will help deepen the understanding of urban decision-makers on the value of M-ULS and also provide a unique insight to solve the transport problems during the epidemic.

Analyzing the performance of M-ULS is a challenging task. On the one hand, the benefits assessment of M-ULS requires the comparison of multiple scenarios such as normal situation, epidemic outbreak and M-ULS implementation. On the other hand, market factors such as demand, average transportation cost, and delivery time determine the market share of M-ULS (Hu et al., 2020b) . In particular, restrictive and incentive policies dynamically affect supply and demand in the freight market, which makes modelling more difficult (Cui and Nelson, 2019) . In short, the simulation of the M-ULS includes nonlinear and dynamic behaviors and feedbacks among multiple stakeholders in multiple scenarios. The innovation lies in two aspects. First, M-ULS was introduced as a unique means to try to break through the plight of city logistics under the COVID-19 epidemic, which also provided a new driver for the practice of M-ULS. Second, a set of effective macro-micro incentive policies were established to promote M-ULS, so as to provide effective support for decision-makers to deal with city logistics issues.

The remainder is structured as follows: Section 2 reviews the state of the art of the relevant literature; Section 3 proposes how the SD method is applied to analyze the impacts of M-ULS on city logistics; Section 4 discusses the key findings; and finally, Section 5 presents the conclusions and avenues for future research.

The dilemma of city logistics is becoming particularly prominent during the epidemic J o u r n a l P r e -p r o o f outbreaks. On the one hand, the limited workforce inevitably leads to the delivery delay, and the scarcity of necessities brings huge deprivation cost to the residents (Holguín-Veras et al., 2013; Singh et al. 2020) . In particular, with a surge of online shopping, door-to-door deliveries also skyrocket, thus weighting more burden on goods distribution (Sheth, 2020) .

On the other hand, with the sharp rise in operating cost, the logistics companies are struggling to survive the epidemic outbreaks (Ivanov, 2020) . For instance, it was reported that the salaries of freight drivers rose about three times during COVID-19 outbreaks (The State

Council Information Office of PRC, 2020). More importantly, the epidemic has exacerbated the infection risk of distribution personnel, further reducing the available workforce (Lemke et al., 2020) . Generally, the current city logistics system cannot effectively respond to the threat of a pandemic outbreak like COVID-19.

Traditionally, the literature on city logistics under epidemic outbreaks has mostly focused on the humanitarian logistics issues, such as the location of temporary distribution facilities, relief distribution strategies, transport policy-making, etc (Dasaklis et al., 2012) . A series of indicators, such as demand fill-rate, delivery time, deprivation cost and transportation cost, have been put forward to analyze the city logistics performance in terms of effectiveness and efficiency (Huang et al., 2015; Banomyong et al., 2019) . However, few studies have analyzed the impacts of epidemic outbreaks on commercial logistics, which cannot provide an effective decision-support framework for solving the problems of commercial products transportation.

In addition, the previously studied cases, such as SARS, Ebola and Influenza, have regional characteristics and the city logistics problems exposed have not yet aroused wide resonance (Queiroz er al., 2020) . However, during COVID-19 outbreaks, serious city logistics issues are prevalent on a global scale. Moreover, the research on the epidemic transmission risk during transportation remains a blind spot. While the recent work of Yu et al. (2020) has analyzed the number of susceptible people in the transportation process, it mainly focused on the transport of medical waste rather than the commercial product. Hence, it is urgent to systematically analyze the impacts of epidemic outbreaks on the city logistics performance and develop highly intelligent and contactless urban freight transport modes.

J o u r n a l P r e -p r o o f

The basic idea of the M-ULS is to directly transport goods from the out-of-town logistics parks, warehouses, factories or consolidation centers to downtown areas via the retrofitted subway system (Hu et al., 2020a) . The final customers of M-ULS service, namely the users of M-ULS, include third-party logistics carriers, hypermarkets, enterprises, hospitals, households, etc. M-ULS, as a special form of ULS, is similar to other forms of ULSs in terms of network planning, system operation and benefit analysis (Chen et al., 2017) . Currently, the research of M-ULS has covered the whole transportation process including freight volume forecasting, packing strategies, train scheduling, freight allocation and transportation, etc.

The feasibility of M-ULS has already been analyzed on many national projects in Italy, Japan, England, France, China, Netherlands and America, respectively (Egbunike and Potter, 2011; Kikuta et al., 2012; Motraghi and Marinov, 2012; Cochrane er al., 2017; Ozturk and Patrick, 2018; Zhao et al., 2018; Gatta et al., 2019) . The cost-benefit analysis illustrates that M-ULS exhibits huge advantages in delivery time and transportation cost, compared with traditional ground transportation (Visser, 2018) . The implementation of networked M-ULS can also promote sustainable urban development and reduce the negative externalities of city logistics (Dong et al., 2018; Dong et al., 2019) . According to Hu et al. (2020a) , the Beijing M-ULS project can reduce the transportation-related emissions of CO 2 and NO x by 4.52 million tons and 18.37 thousand tons during 2021 to 2035. Similarly, the results of Dong et al. (2019) indicates that the emissions of PM are reduced by 64.2% with the application of ULS in Beijing. However, few studies have analyzed the impacts of M-ULS implementation on the city logistics performance under the emergencies such as COVID-19 outbreaks. In addition, while M-ULS will replace jobs of truck drivers as with other forms of driverless transportation mode, the development of M-ULS will create many new jobs in a variety of fields, such as manufacturing of automatic logistics equipment and operation of smart logistics (Chen et al., 2017) .

As for the technical feasibility, great progress has been made in the vehicle automation design, advanced equipment manufacturing, intelligent control and other aspects of M-ULS.

The key technologies matching the operation of the subway system have fully met the J o u r n a l P r e -p r o o f implementation requirements (Shahooei et al., 2019) . The current underground engineering construction technology can also greatly alleviate the negative impacts of M-ULS construction on the operation of the subway system.

M-ULS can realize intelligent operation in the whole process of freight transport.

Referred to Hu et al. (2020a) , the operation process of M-ULS is illustrated in Fig. 1 . Goods are transported by driverless freight trains in the subway tunnels and then distributed and transferred within the M-ULS network to reach the terminal nodes. Finally, goods can be delivered to customers in a variety of forms, such as automated guided vehicles, self-service cabinets and drones, or manual delivery, combined with the ground conditions and specific needs. The M-ULS operator will adjust the freight scheduling appropriately based on the real-time conditions in the freight market. Past ULS projects, such as the London Mail Rail System and OLS-ASG, have provided invaluable experience for the M-ULS implementation. On the one hand, the lack of clear and sustained policy support is one of the important reasons for the failure of ULS projects (Visser, 2018) . However, the current policy and research are merely advocacy and encouragement, do not go deep into the operational, coordinated and phased implementation level. On the other hand, many ULS projects only serve dedicated purposes, i.e., the transport of flower or letters and parcels, which not only weakens the flexibility of ULS, but also fails to generate huge benefits to attract users (Egbunike and Potter, 2011) .

The government's financial support, such as infrastructure investment and operating subsidies, is conducive to promoting the networked operation of M-ULS and realizing the economic sustainability of the system (Zheng et al., 2020) . The policy system of sustainable J o u r n a l P r e -p r o o f logistics development can also provide some enlightenment for the incentive policies of the M-ULS, and some can even directly apply to or guide the implementation of M-ULS. The widely implemented measures, such as taxation, travel restriction policies and environmental regulatory policies, have great effects on promoting the development of green logistics (Van Hassel, 2016; Park et al., 2016; Holguín-Veras et al., 2018; Vanelslander et al., 2019) .

However, a systematic policy support framework for the M-ULS implementation has not been established. In particular, a set of city logistics emergency strategies that effectively coordinate M-ULS operation and ground transportation have not been developed to address the threat of COVID-19 outbreaks and other future emergencies. Of course, unclear benefits analysis also hinders the policy formulation.

SD method, originally developed by Forrester (1956) , can combine both the qualitative and quantitative analysis to understand the complex systems with inherent dynamics, i.e., the system performance evolves over time and depends on feedback loops. Compared with other management science methods, the advantages of SD method are prominent. On the one hand, the variables of the SD model vary over time based on the feedback mechanism of the system, which is conducive to simulating the long-term and periodic problems (Mingers and White, 2010) . On the other hand, the data needed in the modeling process is easily accessible, and can be obtained from a variety of sources, such as statistics, literature, and even people's experience (Puylaert et al., 2018) . Therefore, this paper adopted SD method to analyze the role of M-ULS in breaking the bottleneck of city logistics, and further to explore the government's response mechanism to transportation difficulties during the epidemic outbreaks.

SD is widely used in the field of city logistics, such as transportation policies analysis and innovative transportation simulation (Lewe et al., 2014; Sabounchi et al., 2014) .

According to Sterman (2000) , an SD model was established, and the analysis steps as shown in Fig. 2 were carried out.

J o u r n a l P r e -p r o o f First, the background of the case was analyzed to propose the basic parameters and assumptions for the SD model. Then, the system boundary was defined to identify the main variables included in city logistics. Thus, the causal loop diagram (CLD) and the stock-flow diagram were established to describe the relationships between the main variables. Next, we validated the model and simulated the scenarios. Finally, the simulation results were discussed in combination with the M-ULS operation strategies and the transportation policies to propose the coping mechanism.

The real urban and logistics data of Wuhan Central Activity Area (CAA) were selected as the empirical background. CAA covers an area of 530 square kilometers and had a population of 6.1 million in 2019, accounting for 54.5% of the city's population (Wuhan Municipal Bureau Statistics, 2020). It is an area with high population density, reaching 11,510 people per square kilometer. The city's freight volume was 417.8 million tons in 2019 (Wuhan Municipal Bureau Statistics, 2020). Based on the bubble weight ratio, the delivery volume had reached 92.7 billion pieces, about 23 pieces per person per day. According to Hu et al. (2020b) , 20% to 25% of the total freight is consumer goods, and the number of parcels per person is as high as five pieces per day. Normally, there are more than 30,000 couriers and 50,000 trucks are involved in the city's logistics, so the urban transport is under great pressure and transportation disruption often occurs (Wuhan Municipal Bureau Statistics, 2020).

The COVID-19 outbreak in 2020 has had a huge impact on Wuhan's transportation and logistics. In response to the changes in the epidemic, local governments have implemented various traffic control measures. On 23 January 2020, Wuhan announced a policy to shut down the city, while subway, bus and other transportation infrastructure were suspended.

Limited transport capacity gives priority to medical supplies and necessities. Even with all of J o u r n a l P r e -p r o o f China's efforts, the supply capacity is still clearly inadequate, not to mention the fact that other non-essential goods can hardly be delivered in time. According to the data of 33 main e-commerce websites in Wuhan, the intra-city delivery time was as high as one to three days (Bendibao, 2020) . As the situation worsened, Wuhan imposed stricter community lockdown policies on 11 February 2020. Each community centralized the distribution of daily necessities to the residents. By mid-March, the epidemic had eased, and the community lockdown was lifted. The city's public transport system was gradually returning to work. On 8

April 2020, Wuhan announced the lifting of the lockdown policy.

Since the official data has not been made public, this study investigated residents' daily needs and deprivation cost of 416 families in CAA during the epidemic period. The results of the survey are shown in Appendix. Combined with the survey data samples, it was assumed that the packages were all cubes with a side length of 30cm, which served as the basis for modeling. April 2020, about 1 to 2.5 parcels, which is obtained based on the survey and reasonable prediction of Wuhan Statistics Bureau data. During the epidemic, the parcels were mainly essential items for residents' daily life. After 23 January, due to the policy of closing down the city, the number of times residents went out for shopping decreased, and PV decreased significantly. Until 11 February, after the community closure, a large number of relief supplies rushed to Wuhan, resulting in a gradual increase in PV. As the lockdown gradually lifted, workers returned to work, further boosting the city's overall freight demand. According to data released by HuoLaLa (2020), a large urban distribution company in China, before Wuhan's closure, the city shipped 50% of its usual amount of consumer goods. To simplify the model, it is assumed that after 11 February, PV increases linearly and reaches 2.5 parcels per person per day on 8 April. In the case of non-epidemic, during this period, considering that many logistics enterprises are closed during the traditional Chinese Spring Festival, it is assumed that PV is 1.25 parcels per person per day from 23 January to 31 January, and increases to five parcels per person per day after the festival. In addition to mitigating the negative externalities of city logistics, M-ULS also have J o u r n a l P r e -p r o o f significant advantages over traditional surface transport in responding to urban emergencies such as COVID-19. Highly intelligent transportation system can greatly reduce the manpower demand in every link of urban distribution, including the whole process from distribution center to terminal delivery. In this way, the necessary disinfection and quarantine procedures are reduced, and the delivery speed is improved. More importantly, the risk of transmission of the virus through contact with cargo personnel was significantly reduced. 

In order to analyze the challenges faced by city logistics during the outbreaks of the epidemic, the proposed model mainly included three factors: truck transportation process, M-ULS operation process, and the performance of city logistics. Balancing loops B3 contains a set of negative loops, indicating that the freight capacity of the M-ULS will limit its freight volume. When freight demand exceeds the maximum capacity of M-ULS, delivery delays will limit the AM and then decline the freight orders of M-ULS.

It is worth noting that, like other green logistics technologies, the AM is strongly influenced by local government policies, as is trucking. The government can regulate and supervise the freight market by providing subsidies and guiding policies.

The stock-flow diagram was established by integrating and expanding the key feedback loops (Fontoura et al., 2019) . The three subsystems were described separately due to the complexity of the overall SD model hierarchy. Table 1 lists the main variables and their reference sources. 

This subsystem depicts the daily operation of ground truck transportation which is affected by the supply-demand of freight market, as shown in Fig. 6 .

J o u r n a l P r e -p r o o f (1) 

On the demand side, the freight demand of trucking (FDT) and the actual freight volume of trucking (FVT) are determined by both the intraday orders and the unmet orders of the previous days, as shown in Eq. (3) and (4). The model also introduced a time sequence for delivery, which depends on the priority of goods. Priority is given to orders that have not been delivered before. The earlier the order is placed, the higher its priority is. In addition, the distribution of medical waste should be given the highest priority under the epidemic situation.

The unit delivery time of trucking (UDTT) is the average delivery time of all orders completed on the same day. Due to possible shipping delays, UDTT is determined by truck transportation order structure and the actual delivery time of each order. The delivery time of orders in the model was set as one to four days. For example, if the new order can be delivered on time, the delivery time will be one day. If there is an undelivered order on the previous day, the delivery time of that order is increased by one day. Table 3 . Since passenger traffic is suspended during the epidemic outbreak and M-ULS will be fully serviced for freight, allowing FHM to be set at 1.5 times its normal level. Then, with the resumption of urban traffic, the FHM will return to the normal level. 

Transportation risk is mainly concerned with the risk of epidemic transmission, rather than safety accidents. It can be defined as the number of susceptible people (NP) caused by the person-to-person contact during transportation. The susceptible-exposed-infected-removed model proposed by Wu et al. (2020) was introduced to simulate the development trend of COVID-19. According to Yu et al. (2020) , the NP depends on the probability of human-to-human transmission risk (PS) and exposure people during transportation (ET), as shown in Eq. (13). TNP is the accumulated NP during the simulation period.

(13)

Validation test of SD model, such as model structure and behavioral tests, is necessary before simulation (Sterman, 2000) . First, the model structure, such as stock and flows variables, and equations, was tested using direct inspection and comparing with the knowledge about the structure of the generic logistics operation. For dimensional consistency, the Vensim 7.2 software build-in tool was used to check the model units. Then, the variables of Existing number of infected people and Total number of infected people were selected for behavioral validity test based on the actual data reported by Wuhan municipal government from 23 January 2020 to 8 April 2020. The comparison between the simulated data and historical data of the two variables is shown in Fig. 9 . Both sets of data show a good fit for data from 12 February, with errors of J o u r n a l P r e -p r o o f just 1.5% and 5.4%, respectively. The poor fit of the initial data is due to the fact that on 12

February, Wuhan updated the diagnostic methods, re-examined the suspected cases, and modified the previous diagnosis results, resulting in a large difference between the number of reported cases and the actual cases. Therefore, in addition to data fluctuations caused by emergencies, the simulation results conform to the common SD model testing standard (Emberger and Pfaffenbichler, 2020) , indicating the good validity of the model. Table 5 Overview of the scenarios. In S5, the role of government subsidies in promoting the implementation of M-ULS and improving the overall city logistics performance is discussed by changing the value of SI.

Where SI under S5 is assumed to be 0.85 and 1 in other scenarios.

In Fig. 10 , the city logistics performance under S1 and S2 is compared. There is no COVID-19 epidemic in either scenario, or the policy of city closure is also not implemented.

S2 is superior to S1 in all aspects, indicating that M-ULS significantly improves city logistics.

Of course, this has been proved in Dong et al. (2019) and Hu et al. (2020) . This paper only used the data of Wuhan to verify again that M-ULS is an effective supplement to the existing city logistics. Fig. 10 . Simulation results of S1 and S2. (2-column) Fig.10 (a-b) shows that, compared to S1, the implementation of M-ULS contributes to an increase in urban freight capacity, resulting in an increase in DR and a decrease in UDT. In Fig. 10 (c) , the TDC of S2 reaches $594.8 million, which is 61.5% less than that of S1.

However, the TTC difference between the two is not significant, as shown in Fig. 10 (d) . This is due to the limited capacity of M-ULS under mixed passenger and cargo transportation.

Even full load operation can only meet 13.7% of the city's total freight demand. In spite of this, M-ULS still shows huge external benefits based on the comparison results of S1 and S2. great market competitiveness. Because its transport scale effect reduces unit transport cost, although the advantage in delivery time will diminish with its widespread application.

The comparison between S1 and S3 can reflect the impact of city closure policy on city logistics, as shown in Figure 11 . There is no implementation of M-ULS in either scenario. Fig.   11 (a) shows that the traffic control measures caused by the epidemic have greatly reduced DR.

During the closure of the city, the demand for freight decreased significantly, but the huge gap in freight capacity caused by the shutdown and other factors led to the continuous decline of DR, which dropped to 36.6% on 3 February. With the introduction of the government freight incentive measures and many volunteers to participate, DR has been greatly enhanced.

Between 23 February and 1 March, all the orders of the same day have been processed in time.

After the closure lifted of communities and the resumption of work, the demand for freight continued to grow, but the existing logistics model was still unable to meet the demand. DR continued to decrease until the city was unsealed only 26.4%. As a result, delivery delays were the norm during outbreaks, with UDT as high as 3.4 days on 8 April as shown in Fig. 11 (b). Fig. 11 . Simulation results of S1 and S3. (2-column) Fig. 11 (c) reflects the impacts of city closure on TDC. From 23 January to 12 March, the "S" shaped TDC curve indicates that the parcels can be delivered timely as the city's freight capacity increases. But then the existing trucking system hit a bottleneck. TDC shows a significant upward trend, reaching $2.6 billion during the city closure, 73.3% higher than J o u r n a l P r e -p r o o f normal. Fig. 11 (d) depicts TNP during the city closure. The spread of the epidemic showed a decreasing trend with the continuous closure of the city (Wu et al., 2020) , which was also reflected in the TNP curve. Although freight demand gradually increased, the TNP growth trend gradually flat. Therefore, controlling the number of people involved in freight transportation during the initial phase of the epidemic is a decisive factor in reducing transport risks.

In general, compared with the normal situation, during the duration of the epidemic, the contradiction between the growth of freight demand and the limited freight capacity of cities is more prominent. The traditional city logistics distribution mode, which relies on the ground road and human driving, is seriously inadequate in dealing with emergencies. Particularly, increasing the number of lorries and drivers to eked out more capacity is an obvious last resort in an epidemic. Although it can reduce the deprivation cost caused by delivery delay, it will increase TNP greatly. Therefore, the COVID-19 outbreak has exposed severe urban distribution bottlenecks, and M-ULS is an effective complement to the labor-intensive logistics distribution method. performance by comparing the city closure scenarios of S3 and S4. Fig. 12 (a) shows that the trend of DR appears in a "U" shape in S4. Due to the low market share of M-ULS, DR shows a downward trend in the initial stage of city closure, which dropped to 64.6% on 26

January. However, with the increase in capacity, M-ULS was highly attractive in transportation cost and delivery times, and DR subsequently rises sharply. In addition, Fig. 12 (b) also shows that the application of M-ULS is an effective supplement to truck transportation. UDT is going down fast and remains around one day. However, this trend will eventually attract orders beyond the M-ULS capacity limit, leading to delivery delays where the UDT curve vibrates slightly. 228.9 thousand people, about 48.8% less than that of S3. The TNP curve quickly becomes stable after M-ULS is widely adopted, which reflected this advantage. In contrast, TNP grew rapidly when M-ULS market share was low, that is, at the early stage of city closure.

To sum up, compared with ground truck transportation, the huge comprehensive benefits reflect the flexibility of M-ULS in dealing with traditional problems of city logistics and emergency situations. However, the tension between the limited capacity of M-ULS and the growing demand for freight will also become apparent. Hence, effective means are needed to optimize the operation of M-ULS and the stable expansion of the network, so as to continuously improve the performance of city logistics. Fig. 13 (a) . Under S5, M-ULS runs at full capacity in just six days, two days earlier than S4.

Moreover, due to its huge market competitiveness, M-ULS has always maintained a high UM after being widely used. 

According to the simulation results, a portfolio of possible incentives was designed for different stages of city closure to promote the application of M-ULS and thus to solve the worsening city logistics. Incentives are depicted in Fig. 14 . 

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