Transit accessibility is an important measure on the service performance of transit systems. To assess whether the public transit service is well accessible for trips of specific origins, destinations, and origin-destination (OD) pairs, a novel measure, the Trip Coverage Index (TCI), is proposed in this paper. TCI considers both the transit trip coverage and spatial distribution of individual travel demands. Massive trips between cellular base stations are estimated by using over four-million mobile phone users. An easy-to-implement method is also developed to extract the transit information and driving routes for millions of requests. Then the trip coverage of each OD pair is calculated. For demonstrative purposes, TCI is applied to the transit network of Hangzhou, China. The results show that TCI represents the better transit trip coverage and provides a more powerful assessment tool of transit quality of service. Since the calculation is based on trips of all modes, but not only the transit trips, TCI offers an overall accessibility for the transit system performance. It enables decision makers to assess transit accessibility in a finer-grained manner on the individual trip level and can be well transformed to measure transit services of other cities.
Public transportation plays an important role in solving traffic problems in urban cities. It is well recognized among transportation planners that transit accessibility is an important measure of the service performance. The Transit Capacity and Quality of Service Manual summarized spatial, temporal, information, and capacity availability factors of public transit systems [
As shown in Table
Summary of transit accessibility measures.
Category | Measure description | Application | Reference |
---|---|---|---|
Physical access to transit | Proximity to transit stops in time or distance | Measuring accessibility for local transit operators in London | Hillman and Pool [ |
Quarter-mile buffers around transit routes | Transit coverage in the Queen Anne Community of Seattle | Nyerges [ | |
Pedestrian average and maximum walking distance to transit stops | Three neighborhood plans for a 23.3 ha site | Aultman-Hall et al. [ | |
| |||
Accessibility to destination | Travel-impedance measurements (e.g., travel distance, time, or cost) | Mass/light rapid transit systems in Singapore | Liu and Zhu [ |
Public transport relative accessibility percentage (transit catchment area and population by transit within 60 min) | 90 sites in the south east of England | Gent and Symonds [ | |
Transit accessibility index (TAI) and transit dependence index (TDI) | TransCAD-based transit accessibility measure (TAM) software tool | Bhat et al. [ | |
| |||
Temporal accessibility | Span and headway of transit service and time-of-day distribution of travel demand | Numerical illustration | Polzin et al. [ |
Space-time accessibility measures with opportunities and human activity-travel behavior | Commercial and industrial land parcels of Portland Metropolitan Region, Oregon | Kim and Kwan [ | |
Dynamic activity opportunities that can be reached within a prespecified time limit with known transit schedules | Southern California Association of Governments megaregion | Lei et al. [ | |
Rate of access poverty among population | Regional transportation plan scenarios from the San Francisco Bay Area | Golub and Martens [ |
Based on the literature review it is evident that
There are some reasons for the gap of the previous studies. In the past, multiple sources of data required to evaluate transit accessibility considering individual travel demands are difficult to collect and consequently extensive efforts are required in order to obtain the useful data. In particular, it is difficult to measure real-world travel demands due to the small amount of household survey data in the past. In addition, many surveys are zone based and unable to describe individual travel behavior. In some cases, public transit operational data (e.g., stops, routes, schedules, frequencies, and hours of operation) may be hard to access and data fusion could become uneasy due to their inconsistent formats in the time scale and data particle size [
Fortunately, the recent advent of data collection technologies, for example, mobile phone signaling data and automated vehicle location, has shifted a data-poor environment to a data-rich environment and offered opportunities to conduct comprehensive transit system performance evaluation. For example, cell phone signaling data have emerged to be a widely used resource to measure both individual travel behavior and network demand, for example, individual human mobility patterns [
This paper is aimed at presenting a new public transit quality of service measure, the Trip Coverage Index (TCI), which takes into account both the trip coverage for transit systems and the spatial distribution of heterogeneous and dynamic individual travel demands. The TCI provides a quantitative measure of transit accessibility on the basis of massive trips collected from mobile phone data. The transit accessibility information is extracted from the Baidu Map with the Python code implementation, for example, the access to transit facilities, transit routes (shortest in time/length and alternatives), transit on-vehicle time, and OD connectivity. The novel measure of transit service performance fills the research gap that the conventional spatial coverage index does not consider the coverage to individual trips or the percentage of travel demands that can be served by the transit systems.
The rest of the paper is organized as follows: Section
In this part, we first propose a new method to acquire the transit route information for millions of trips determined from the mobile phone data automatically based on online map and programing; then a new public transit quality of service measure (TCI) is proposed considering the access to transit facilities, transit routes information, driving routes information, and OD connectivity. The development of the proposed TCI requires several steps and the framework is shown in Figure
Estimation framework of TCI.
In this section, we introduce the mobile phone data and present the methods used to determine trips from the mobile phone data.
The dataset used in this study consists of two tables in the database: one is the base station table and the other is the anonymous table of mobile phone records. The mobile phone record is generated when a device connects to the cellular network in any of the following cases: when the phone makes or receives a call; when the phone sends or receives a message; when the phone is switched on or off; when the user moves from one base station to another; or when the system sends the periodic location update request on the phone, for example, 2 h.
The mobile phone signaling data contain Call Details Records (CDR), which were previously utilized to estimate OD demands in numerous related studies [
In order to infer trips from the mobile phone signaling data, the first step is to filter out noise resulting from one base station to another. The call balancing is conducted by the mobile service provider, which creates the appearance of false movements, and distinguishes users’ stay locations. Once the stay locations are determined, we evaluate the trips as paths between a user’s consecutive locations. To achieve this, we estimate the trips by employing the method of using mobile phone traces data [ Each mobile phone signaling record Then the signaling records are connected into a sequence of records If the signaling record series We evaluate paths between a user’s stay locations at consecutive points, and the stay locations are assumed to be trip origins or destinations.
Calculating the trip coverage indicators requires a database with transit data such as the transit network, road network, operational transit information, and bus stops. Based on those data, we know how many transit lines serve the trips from base station
More and more online map services provide path navigation in China, for example, Baidu Map and AMAP. If the user selects the transportation mode, enters the origin and destination, and chooses a departure time on the map website, it will return the route planning information including the trip distance, trip time, and suggested routes from the origin to destination. Some online map services provide open resources to developers, which are mostly in the form of the Application Programming Interface (API). The API is a set of predefined user applications and the operating system’s function, by means of which programmers can easily achieve the underlying operating system feature development or packages. Launched in April, 2010, Baidu Map API [
As shown in Figure
Web scraping of transit information.
The response of the transit route information from the Baidu Map API contains fruitful information and we just extract the useful information for assessing transit accessibility, for example, the taxi route information and bus route information. An example of response is shown in Table
A sample of the transit route information from the Baidu Map API.
Status | 0 | 0: correct record | |
| |||
Taxi | Taxi_distance (m) | 4,540 | Travel distance by car |
Taxi_travel time (s) | 503 | Travel time by car | |
| |||
Bus scheme 1 | Total distance (m) | 4,584 | Full trip distance |
Total travel time (s) | 2,276 | Full trip travel time | |
Scheme_type | 1 | 1: shortest time scheme | |
Segment1_type | 5 | 5: walk | |
Segment1_diatance (m) | 311 | Walk distance | |
Segment2_type | 3 | 3: bus | |
Segment2_diatance (m) | 3523 | In-bus distance | |
Segment3_type | 5 | 5: walk | |
Segment3_diatance (m) | 750 | Walk distance | |
| |||
Bus scheme 2 | | | Second best scheme |
Generally, there are three segments per scheme in a direct transit route without transfer, which means that the trip distance
The conventional evaluation criterion for the transit service includes the transit spatial coverage area, which is usually estimated using the buffer area covered by the transit route or by the area within a walking distance threshold of a transit stop or transit route [
Based on the aforementioned idea, this paper presents the binary connectivity parameter (
The research concern is the extent of the efficiency and attractiveness of the public transport system compared to private cars. As is known to all, there are many factors that may influence the travel mode choice such as travel time, transit distance, transit fare car parking fare, and weather. However, this paper is not concerned with individual travel mode choice behavior but provides the trip-level assessment of transit accessibility, so we just take into account the travel time and travel distance. On the other hand, the developed index (i.e., TCI) is applied to assess accessibility of public transit for all trips, not only those currently or likely to be using transit and no matter whether he/she owns cars or not. Instead, they can rent a car or take a taxi to reach the destination for those have no access to personal car. Therefore, the trip coverage from base station
Considering there may be serval transit lines or no transit lines serving the trip, we select the maximum value between 0 and
There are some short trips, of which the distance is shorter than the walking distance threshold. In other words, it is unnecessary to take bus for this trip. So when calculating TCI, we only consider the trips with a distance longer than twice of the service access distance threshold, that is, 1,000 m.
The spatial relationship between TAZs and base stations (BSs) can be obtained by the ArcGIS spatial analysis toolbox. The TAZ-BS membership table can be obtained, which is the foundation of calculating TCI from TAZ
TCI can be used to quantify the coverages of origin TAZ
This section provides a tractable numerical example to illustrate the application of TCI to the assessment of transit accessibility. As shown in Figure
An illustrative road and transit networks.
Table
Trip coverage calculation.
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| | | Zone | Zone | Bus line | | | | | | | | | |
1 | 2 | 0 | 1 | 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| | 4 | 1 | 3 | 1 | | 613 | 300 | 1225 | 400 | 2100 | | 1 | |
| | | | 4 | 1/2 | 3700 | 1245/1353 | 300/250 | 2125/2178 | | 4500/3300 | 5500/4650 | 0/0 | 0.000 |
| | | | | | | 1566/1227 | | 1875/1470 | | | | | |
2 | 1 | 1 | 2 | 1 | NA | 2600 | NA | NA | NA | NA | NA | NA | 0 | 0.000 |
2 | 3 | 12 | 2 | 3 | 3 | 3400 | 1198 | 300 | 1550 | 300 | 3800 | 4400 | 1 | 0.773 |
2 | 5 | 8 | 2 | 4 | 3 | | 827 | 300 | 975 | 450 | 900 | | 1 | |
3 | 1 | 7 | 3 | 1 | 1 | 1400 | 613 | 400 | 1225 | 300 | 2100 | 2800 | 1 | 0.500 |
3 | 2 | 15 | 3 | 2 | 3 | 3400 | 1198 | 300 | 1550 | 300 | 3800 | 4400 | 1 | 0.773 |
3 | 5 | 2 | 3 | 4 | 3 | 3200 | 1293 | 300 | 1475 | 450 | 2900 | 3650 | 1 | 0.877 |
5 | 1 | 3 | 4 | 1 | 1/2 | 3700 | 1873/1695 | 1100/700 | 2279/1800 | 250/300 | 4500/3300 | 5850/4300 | 0/0 | 0.000 |
| | | | | | | 1580/1311 | | 1893/1571 | | | | | |
6 | 2 | 8 | 4 | 2 | 3 | 1600 | 945 | 450 | 975 | 300 | 900 | 1650 | 1 | 0.970 |
6 | 3 | 3 | 4 | 3 | 3 | 2600 | 1236 | 450 | 1475 | 300 | 2900 | 3650 | 1 | 0.712 |
Note: NA = not applicable.
The illustrative numerical example helps understand the difference between the proposed measure and the conventional spatial coverage measure. There are two lines and two bus stops serving BS1, while there is only one transit line and one bus stop serving BS2. At the same time, there are two bus lines serving trips of BS1–5 and only one bus line serving trips of BS2–5. It is reasonable to expect a higher level of transit coverage for BS1–5 than that of BS2–5. However, for trips of BS1–5,
Column 15 shows the trip coverage of different OD pairs, that is,
Table
Finally, TCI from TAZ
TCI and travel demand (in brackets).
Origin | Destination | ||||
---|---|---|---|---|---|
1 | 2 | 3 | 4 | | |
1 | NA | 0.000 | 0.500 | | 0.300 |
| | | | ||
2 | 0.000 | NA | 0.773 | 0.848 | 0.765 |
| | | | ||
3 | 0.500 | 0.773 | NA | 0.877 | 0.702 |
| | | | ||
4 | | 0.970 | 0.712 | NA | 0.753 |
| | | | ||
| 0.536 | 0.841 | 0.706 | 0.526 | 0.658 |
| | | | |
Note: NA = not applicable.
The
Similarly, other
TCI also offers a way to quantify the transit service level of OD pairs that require a transfer between transit lines. Equation (
The spatial coverage is the proportion of the area served by transit stops, which can be calculated by the Transit Capacity and Quality of Service Manual [
Zonal data of spatial coverage and TCI.
Zone, | Zone area (km2) | Bus stop buffer (km2) | Spatial coverage | | |
---|---|---|---|---|---|
1 | 0.880 | 0.680 | | | |
2 | 1.026 | 0.283 | | | |
3 | 1.650 | 0.807 | 0.489 | 0.702 | 0.706 |
4 | 1.026 | 0.503 | 0.490 | 0.753 | 0.526 |
In this section, TCI is applied to a case study in Hangzhou, China, to assess the transit accessibility. Hangzhou is the capital and most populous city of Zhejiang Province, China. As shown in Figure
Layout of transit and the average base station coverage radius in terms of TAZs in Hangzhou, China.
The mobile phone signaling data used in this study consist of two tables, that is, the base station table and the anonymous mobile phone records table collected from 4.17 million mobile phone users in Hangzhou over one month between August and September, 2015. The position accuracy of a trip is determined by the coverage radius of base stations. There are 41,823 base stations in the 540 TAZs, and the average BS coverage radius for each TAZ is shown in Figure
The study time periods are AM peak hours (7:00–9:00) and PM peak hours (17:00–19:00). After processing the mobile phone signaling data using the method proposed in Section
Desire lines of OD trips during the AM peak hours.
We also obtain spatial and temporal distributions of the population density using the trip information, for example, origin, destination, and timestamp. As shown in Figure
Spatiotemporal distributions of population density using mobile phone signaling data (September 8, 2015).
7:00
10:00
17:00
20:00
Combining both the mobile phone data and transit information extracted from the online map service, we are able to calculate the TCI for different time of day. The analytical results are as follows: the distribution of
Distributions of TCI of TAZs during peak hours (
Distribution of TCI
Distribution of
Comparison of
As shown in Figure
Comparison of
In order to further explore the sensitivity of the walking distance threshold, acceptable times of transfer, and the weighting factor
Sensitivity analysis for transit network.
Transit network TCI with respect to the walking distance threshold (
Transit network TCI with respect to transfers (
Transit network TCI with respect to
As the acceptable transfer times increase from 0 to 2, the TCI increases both during the AM peak hours and PM peak hours, which is comparable with experience. The results suggest that increasing the transit route crossings would provide a better transit service.
We also explore the interaction between the weighting factor
In this paper, the novel TCI is proposed for measuring transit connectivity and accessibility. It is built on the existing transit service measures and allows us to analyze the transit connectivity and accessibility for massive trips between the origin and destination, as well as the transit coverage from or to a TAZ. This paper is among the first attempts considering the connectivity of trips from point to point and real-world complicated travel demand in a large-scale urban area. The TCI developed in this paper provides the capability to quantify the level of accessibility of the transit system and vary the assessment of transit accessibility with the temporal and spatial change of travel demands.
This paper also presents an easy-to-implement method to acquire the transit route information for millions of trips based on the online map. Since the data is acquired automatically using computer programming, it is possible to easily construct the data repository and analyze large public transit networks.
TCI can be applied to all trips, not only those currently or likely to be using transit, such that TCI is demonstrated as an overall measure of transit accessibility and can be used to measure how the transit system reaches its target, which is to provide services for more potential users.
Through the case study of Hangzhou, we find that fluctuations in the travel demand in different time periods make TCI distributing diversely across the city, which means transit operators should reschedule transit routes in a dynamic way to be consistent with travel demands. The sensitivity analysis is performed to determine how the walking distance threshold, times of transfer, and the weighting factor would impact the network-wide TCI. The results can provide operators targeted measures to improve transit services.
Service access distance threshold
Distance from base station
Travel time from base station
Total distance from base station
Transfer count from base station
Total travel time from base station
Access distance from base station
Egress distance from base station
Origin traffic analysis zone (TAZ)
Destination TAZ
Transit line
Origin base station
Destination base station
Travel demand from base station
Travel demand from TAZ
Trip coverage from base station
Trip coverage from base station
Trip coverage index from TAZ
Trip coverage index of origin TAZ
Trip coverage index of destination TAZ
Trip coverage index of a transit network
Binary connectivity parameter, 1 if a transit line
The authors declare that they have no competing interests.
This research is financially supported by Zhejiang Provincial Natural Science Foundation of China under Grant no. LR17E080002, National Natural Science Foundation of China under Grant nos. 51508505, 51338008, and 51278454, and Hangzhou Municipal Science and Technology Commission under Grant no. 20142013A57. Mr. Yanlei Cui helped in processing some of the data used in this paper, and his assistance is gratefully acknowledged.