Operational planning in China is perhaps more important today than ever before owing to the ongoing expansion of urban rail in the country. As urban rail networks increase in size and complexity, new lines added to them significantly alter both their topologies and operational characteristics. Thus, appraisal of alternative lines from the perspective of operation while planning is crucial. In this study, a method to forecast demands for new lines and obviate the effects of their addition, in terms of overcrowding in urban rail networks, was developed based on smart card data from existing networks. Using the card data and forecasted demand, transfer demand and section load can be estimated through the route choice model, and hence the influence of new lines on the operation of the network can be analyzed. The results of application of the proposed method to a case of line extension of a network in Beijing showed that it effectively prevented overcrowding by fewer interchanges on the line extension. Approximately 63% of passengers desiring an interchange on the target line altered their interchange from the station that had acted as bottleneck to the new interchange. Consequently, the headway of the feeding line was reduced from 6 min to 3.5 min. Hence, the capacity mismatch problem no longer occurred.
Urban rail transit in China is experiencing unprecedented growth at present. By the end of 2016, as many as 28 cities had urban rail services operating over 3800 km, and 228 urban lines spanning 5600 km of rail were under construction in 48 cities across the country. Some of these cities were also building 300 km metro lines at the same time. Ever more lines are being planned as well. Megacities like Beijing and Shanghai have developed metro networks of more than 500 km of rail, and each network will be further expanded to 1000 km in the next decade. In such massive and complex networks, each extra line exerts a complicated influence on the existing network, especially from the perspective of the operation of the network.
Consequently, to maximize the safety, reliability, and efficiency of the transportation system, the concept of operational objective planning has been developed [
Many operational measures are consequently implemented to mitigate this problem. Passenger controls have been applied at 73 stations, which requires detaining passengers at station entrances during morning peak hours. In addition, trains on these suburban lines will run at long headways, even when the signal system allows for short headways. However, these measures limit the capacity of the network and cannot completely solve the problem. As the problem is part of planning, it should be addressed by planning, especially when the operational data are available. This means that when a new line is being planned, its impact on the existing network should be appraised from the perspective of operation—not only based on its effect on network topology, but also on demand distribution. The large amounts of smart card data (SCD) used in urban transits can aid in understanding of the real demand and can be used for such assessments.
Thus, in this study, a method to assess new line planning in a massive rail network from the perspective of operation using SCD was developed. Further, the developed method was applied to evaluate a case of urban line extension planning to determine whether it can reduce overcrowding resulting from a mismatch in capacity among lines. The remainder of this article is arranged as follows: related research is reviewed in Section
A new line has various impacts on an existing urban rail transit network [
To find a suitable method to forecast passenger demand for new rail lines, Preston [
Based on forecasted demand, the ridership on sections of new lines can be calculated through route choice or a traffic assign model, which is an unavoidable step to evaluate the impacts of new lines. Many studies have been conducted in this area. By dividing the route choice process into two steps, a route generation step and a route selection step, Hurk et al. [
The impacts of new lines can also be analyzed from the results of demand studies to provide suggestions for management and operation. However, most existing studies focused on postproject analysis using operation data after the new line opened. Li [
The studies cited above show that significant research has been deeply conducted on demand forecasting and passenger flow distribution, which are preparatory work for impact analysis on network ridership—even using SCD. However, the studies conducted on impact analysis of new lines on existing networks still aim at finding general patterns of the influence of new lines on the existing networks, rather than to guide operational organization during the planning phase. If a new line is intended to solve a specific problem in an existing network, it is necessary to determine whether this can be accomplished in the planning stage. For the case addressed in this work, congestion and long headway occurred on the Changping Line of Beijing subway owing to the capacity-demand mismatch problem. This work investigated whether the southern extension planning of the Changping Line could solve this problem. To this end, using SCD and forecasted ridership, we evaluated the impact of the southern extension planning on the ridership of the congested sections. The results obtained can show whether and to what extent the planning scheme can solve the problem of overcrowding caused by prolonged headways. Thus, a method was developed in this study to closely consider operation in the early phase of planning to improve decision making.
New rail lines change the coverage and topology of an existing network, which leads to changes in the demand distribution across the whole network, especially on lines and stations near the new line. Therefore, demand forecasting and assignment for the new line are crucial for its planning and operational management. The demand forecasting includes boarding and alighting demands for the new station and existing stations and the demand distribution of the new station.
The extension to the Changping Line is located in the service range of the existing network. Therefore, a direct demand model was chosen to predict the station-level demand and integrated with a geographically weighted model to consider spatial dependency. From existing studies, the built environment has verified the relationship with travel demand in urban planning [
Based on previous work [
where
On the basis of the data obtained, a population density of children (0-19), young adults (20-39), middle-aged (40-59), and seniors (>60), areas of different land use attributes, number of feeder buses, line densities of motor vehicle roads, and nonmotorized vehicle lanes were chosen as built environment factors.
The models were calibrated to forecast the ridership of new stations. For new stations, the same set of independent variables can be extracted from the feasibility study report of new lines. Substituting these variables into calibrated models, we can forecast the demand of new stations to a desirable accuracy level.
Compared with the existing methods, this study proposed a comprehensive direct model to integrate the impacts of spatial attributes and station function type on the ridership of new stations. Besides, because the proposed model is based on SCD, it allows analyzing and forecasting ridership in fine time granularity, such as ridership for each hour, which is beneficial for forecasting ridership during peak hours and for any further analysis using high time resolution demand, such as the headway evaluation in this work. Then, a GWR model for all stations is implemented by taking the ridership during 7:30 to 8:00 as an example and the local R-square range from 0.46 to 0.85, as shown in Figure
Comparison of existing model and proposed model.
The influence of new line demands on an existing station can be divided into induced demand and diverted demand [
Regarding induced demand, we chose indicator of accessibility to describe the impact. Accessibility of an existing station
Accordingly, the induced demand of existing station
where
As for the diverted demand of existing station
The alighting demand at the existing station generated by the new one is calculated in a similar manner to boarding demand.
Finally, the demand of existing station
Stations can be classified into different groups based on land use types, where boarding and alighting demands in the same group follow the same pattern. This assumption is also applied to demand distribution for new stations. Thus, we can calculate the demand distribution of each new station according to the attraction pattern of each group. The factors influencing demand distribution are the number of stations along the shortest path between stations, transfer times, and the total daily alighting demand of the destination station. Supposing that station
where
The origin-destination (OD) demand between new stations can be obtained after normalizing the distribution proportion using
where
Compared with the traditional gravity model, the proposed model is a direct method to calculate demand distribution, which is easy to conduct without fitness reduction. Furthermore, the proposed model can make full use of existing OD distribution data and is suitable for demand prediction of fine time granularity using SCD. Besides, as an impact factor of station demand, land usage type is incorporated into the model to improve the model’s performance. Then, we take 70% ridership data during 7:30 to 8:00 as training data and 30% as predicting data, and a dual-constrained gravity model and the proposed model are conducted. The Pearson correlation coefficients (PCC) of predicted results and real OD demand are calculated. For the gravity model, the PCC is 0.639, and the PCCs calculated by the proposed model range from 0.592 to 0.751 for the nine group pairs. These results indicate that the performance of the proposed model is as good as the dual-constrained gravity model, while being easier to conduct.
Based on forecasted boarding and alighting demand as well as the demand distribution of new stations, OD matrix for new lines at different periods can be obtained. With regard to passengers’ choice of paths and the final section load distribution, it is necessary to create a trip assignment model to simulate a passenger’s path choice behaviors to infer the spatiotemporal distribution of network demand.
The main factors influencing a passenger’s route choice are travel time, transfer convenience, level of service, and random factors [
where
The shortest path is not suitable here on the complex network, and thus, we choose the
where
In December 2016, the Beijing subway network consisted of 18 lines covering major travel corridors, with a total length of 574 km over 345 stations, as shown in Figure
Southern extension planning of the Changping Line.
Before Changping Line opened, trains running on the two sections connected to the Xierqi station were full during morning peak with little surplus capacity. Once it opened, although many operation strategies including regular passenger controls for five stations of Changping Line and minimum headway of 2.5 min for Line 13 are conducted simultaneously, Line 13 still cannot cope with transferring the ridership from Changping Line. Hence, Changping Line is forced to run with 6 min headway, while the minimum headway is 2 min. Some sections of this suburban line became the most crowded sections of the network. For Changping Line, a paradox is that overcrowding occurred with available capacity. To solve this problem, the planners wanted to extend Changping Line to the central city and connect it to Line 10 (in blue), to reduce the load of Line 13 and decrease the headway on the Changping Line. Could this goal be achieved? Would the same problem occur in Line 10? The case study answers these questions.
Implementing the trip assignment model with the SCD for a Monday in March 2016, the section load distribution of the network from 7:30 a.m. to 8:00 a.m. was estimated, as shown in Figure
Section load distribution for the period 7:30 a.m. to 8:00 a.m.
Figure
Destination distribution of boarding passengers.
Figure
Demand distribution on extended Changping Line and part of Lines 10 and 13.
The boarding ridership and section load of the extended Changping Line are shown in Figure
Section load along the Changping Line and the south extension line.
Capacity-demand imbalance occurred at transfer station Xierqi, and the interchange demand during a given time is shown in Figure
Transfer passenger flow at Xierqi station for the period 7:30 a.m. to 8:00 a.m.
Before extension line
After extension line
Xitucheng becomes a transfer station after the extension, and Figure
Interchange demand after the extension.
As shown in Figure
Section load on Changping Line.
Urban rail transit as a capital-intensive project remains virtually unchangeable once built. Therefore, it is crucial to know in advance whether the aims of planning transit routes can be successfully achieved. This is the philosophy of planning for operation, which is more important for new lines added to a large and complex rail network. In such a circumstance, the objects of the planned lines are specific, while the unreliability of demand is limited. In this study, the authors developed a method based on demand forecasting and the route assignment model to provide an ex-ante appraisal of a case of line extension to the Beijing subway. Using SCD from the existing network in combination with the forecasted demand for the new line extension, the proposed method was applied to distinguish and quantify the reasons for overcrowding on the line before it was extended and to assess the effect of the extension from the perspective of operation. The results revealed the conclusions below.
An analysis of the pre-extension scenario showed that Changping Line ended at a high-occupancy station, Xierqi on Line 13, and brought 72% of trains filled with passengers looking to change to the in-bound direction of Line 13 during morning peak hours. The spatial distribution of these passengers showed this pattern clearly. But when in-bound trains on Line 13 arrived at Xierqi station, they were overloaded with a load factor of 113%. Although the interchange passengers had been squeezed into these trains, more passengers had been left stranded on the platform. To prevent this state of affairs, Changping Line was run with a headway of 6 min, although its actual line capacity was a 2 min headway. This extended headway in turn aggravated the overcrowding on the Changping Line, making some of its sections the most crowded in the entire network. This is instructive for urban rail network planning in general.
When extended to the circle line in more central areas, Changping Line could run more trains per hour, which reduced crowding on both Changping Line and Line 13. The new line increased the connectivity of the network and provided more choices of routes to passengers. Residents could go directly to central areas with fewer interchanges. In this case, the interchange demand from Changping Line in the in-bound direction at Xierqi station was reduced by 63% with the extension. The saved capacity resulted in more passengers being accepted from Changping Line in the same time span, and more trains could run on it. Thus, the headway could be safely reduced to 3.5 min, and the average load factor during peak hours was reduced to 70% for the most crowded section on Changping Line and 95% for Line 13. This line extension planning hence was effective. It increased interchange demand and section load at the cross-section with Line 10, but the overall occupancy was acceptable, and the capacity mismatch problem did not occur at the new crossing. The extension only went one station after crossing Line 10. Thus, a real concern was whether the original problem at the old end crossing will be brought to the new crossing by the extension. According to the results, the remaining capacity on the train on Line 10 was sufficient to cater to interchange demand from Changping south extension.
Operational objective planning is important for new line design, especially for solving existing operational issues in a complex network. The models proposed in this study provide a valid method to evaluate a line design scheme from an operational perspective using SCD, which is helpful for policy making. According to the results of this case study, passenger control measurement for the stations on the Changping Line can be canceled after the extension. Besides, the findings stated above show that the suburban line should terminate on the downtown loop line, and this connection pattern has been used in several urban rail transit systems, such as in Tokyo and London, or even pass through the downtown area in New York. Consequently, for the design of a suburban line that mainly serve commuting trips, it should terminate in or pass through the downtown area in practice.
The main limitation of this work is that the trains’ timetables were not considered in the demand assignment model. This affects the accuracy of the calculation of section load and the load factor of the trains. Moreover, the forecasting and distribution of the demand induced by the extension were relatively simple and did not refer to the integrated transport model. The third weakness of this work is that the SCD used was influenced by passenger control measures, and the entry time recorded by the card was not the actual boarding time of the passengers. This also influenced the estimation of the train load factor. These limitations will be addressed in subsequent research.
The smart card data (SCD) used to support the findings of this study were supplied by Beijing Transportation Information Center under license and so cannot be made freely available. Requests for access to these data should be made to Beijing Transportation Information Center ((+86) 010-57079655).
The authors declare that there are no conflicts of interest regarding the publication of this paper.
This work was supported by the Beijing Municipal Science & Technology Commission, Special Program for Cultivation and Development of Innovation Base in 2017 [Grant no. Z171100002217011] and the Natural Science Foundation of Beijing Municipality [Grant no. 17H10148].