**3. Case Study**

This Baishazhou area (Location: 30.42◦~30.53◦ N, 114.25◦~114.30◦ E) in Wuhan was selected as the case study of commuting simulation based on the following considerations: first, Baishazhou is a new area of Wuhan, which is mostly residential in function. Therefore, the study of the impact of commuting on tra ffic condition in this area is of practical value. Second, since the area is still under construction and development, follow-up observations are possible to identify the di fferences between the simulation results of various planning programs and the tra ffic condition in reality. Thus, the proposed optimization and improvement measures may be of grea<sup>t</sup> practicality. Finally, as there is a city-level artery road in this area, i.e., the Baishazhou Avenue, the overall urban layout is distributed along the road in a belt-like shape. With frequent and extensive interactions with the surrounding areas and evident concentration of vehicle tra ffic, the area is deemed a valid case to evaluate the e ffectiveness of the model.

#### *3.1. Data Acquisition and Processing*

After the processing of mobile phone data is completed, the most important step is to allocate the residence and work places of residents at each hour to the space units. This is realized by overlapping aerial photographs, vector electronic map, and existing roads onto previously generated space units. Working with a huge dataset like mobile phone data, the number of time division, and space unit division by different hours of a day may lead to an exponential increase in total statistical size. Therefore, except for the case study area, the division of urban space is minimized to reduce the total number of spatial units. Finally, 34 space units were generated (see Figure 3). Based on these space units, statistics were extracted for the four rush hours on each workday morning. Among these units, plots No. 1 to No. 27 were taken as the core research objects, in which commuting data of residents were acquired. Since the accurate travel routes in other plots cannot be obtained without a fine division, these plots were selected only as destinations but not origins.

**Figure 3.** Road network and division of urban spatial units. (**a**) The original blocks and road network; (**b**) Simplified spatial units of land use and road network; (**c**) Names of key Roads and spatial units.

Using Monday to Friday every week as the time periods in the present study, temporal features of the residents' commute were obtained. In regular commuting, residents' travel from home to work in the morning rush hours and then from work to home during the evening rush hours. In order to reduce the amount of data in space unit division, the division of land for work is simplified. Therefore, only the commuting of residents during the morning rush hours, i.e., the four hours from 6:00–10:00, were considered in the present simulation. Using the base station data in the four hours of 6:00, 7:00, 8:00, and 9:00, the number of plots at 11:00, the number of travels at each hour and at each plot were generated and further generated an OD matrix (Table 3). The number of residents' travels in each core space unit at di fferent hours separately was acquired (Figure 4). The ranking of the numbers for the four time slots was 7:00 > 8:00 > 6:00 > 9:00, which is consistent with our daily experience: since most employers in China set working hours between 9:00 to 17:00, residents leave home for work between 7:00 to 8:00 to reserve enough time for commuting even in face of the possible tra ffic jam during morning rush hours. Therefore, this period is the most popular departure time for commuters.

**Table 3.** Sample statistics of travel volume at each hour and at each plot.


**Figure 4.** Comparison of residents' travels at di fferent hours in di fferent spatial units.

#### *3.2. Model Simulation and Result Verification*

Figure 5 presents real-time screenshot images at several time nodes during the running of the model. As the visual interface could not o ffer quantified tra ffic features, road intersections are numbered and the number of Agents at each running cycle is obtained in order to detect the occurrence, time, and level of tra ffic congestions. The numbering of road intersection is shown in Figure 6a.

*ISPRS Int. J. Geo-Inf.* **2019**, *8*, 313

**Figure 5.** Real-time screenshots during model running and simulation. (**a**) Run time 30 s; (**b**) Run time 900 s; (**c**) Run time 1800 s; (**d**) Run time 3200 s.

**Figure 6.** Numbering and distribution of road intersections. (**a**) Numbering and distribution of existing road intersections; (**b**) Numbering and distribution of planned road intersections.

Finally, the number of vehicles at each intersection in the study area was calculated in various periods each with a 90-s duration to obtain Figure 7. As shown by the changes in the number of agents at all intersections during various time periods, although differences can be seen in terms of the total number of commuter residents, the number of commuter residents on each plot, and the destinations of residents, the overall line charts generated for the four hours of study demonstrate a consistent pattern, representing similar features in the commuting of residents at each hour. Specifically, for road intersections, the peak in the line chart represents the maximum number of traffic generated, and the duration of time indicates the occurrence of congestion. Therefore, it can be seen that the most obvious congestion occurs at Intersection 10, i.e., the intersection of Baishazhou Avenue and the Third Ring Road. Other more congested intersections include No.6, No.9, and No.37.

**Figure 7.** Changes in the number of agents at intersections.

Furthermore, the causes of congestion at each intersection can be analyzed. Combining the congestion process presentation at each intersection in the visual interface, and the number of residents departing from each block in different time periods (Table 3). Traffic in the surrounding area along the Baishazhou Avenue consists of two parts: commuting traffic from the area adjacent to Intersection 10 (the Qingling Interchange) to Plot 27 (the South Lake Area) and Plot 31 (the Optics Valley Area). While traffic in Intersection 37 (the Meijiashan interchange) comes from commuting to Plot 31 and Plot 30 (the Xudong Area). As a result, the commuting traffic flows have grea<sup>t</sup> impact on the road intersections near the two interchanges. In addition, as cross-river traffic in the entire area is still mainly directed to Plot 34, tension in traffic is mostly concentrated along the cross-river bridge (the Baishazhou Bridge) route.

Chinese web map providers, such as Gaode, Baidu, etc., provide not only navigation information, but also traffic forecasts based on their historical traffic data and projections of traffic conditions at different periods of a day. In the present study, traffic forecasts for the case study, the Baishazhou area, at the four time nodes, i.e., 7:00, 8:00, 9:00, and 10:00, are extracted from Gaode map as shown in Figure 8. Since these traffic forecasts are generated based on historical data, they can be considered as road traffic conditions with the highest probability of each road in the past years, and therefore, we used them in the study to verify the results of the model simulation.

**Figure 8.** Traffic forecasts for the four studied hours based on the big data from Gaode Map. (**a**) 7:00 traffic conditions; (**b**) 8:00 traffic conditions; (**c**) 9:00 traffic conditions; (**d**) 10:00 traffic conditions.

Since Gaode's data is only accurate to the hour and cannot be matched with the temporal granularity of tra ffic conditions in this simulation, comparisons can only be made on a similar time precision. Tra ffic at the end of each hour of simulation was used for the comparison, which means the simulation result for tra ffic starting at 6:00 was used for comparison with the tra ffic forecast at 7:00, and so on. It can be seen from the tra ffic forecast for Gaode that it covers only the city artery roads and above, but not the secondary roads or below, and the dark red, red, yellow, and green colors in the diagram represent increasingly better tra ffic conditions, from serious congestion to smooth flow. Therefore, it can be seen that the tra ffic condition is the worst at 8:00 when a dark red section appears at the Qingling Interchange at the intersection of Baishazhou Avenue and Third Ring Road, and the section is in yellow at the other three time nodes, indicating slight congestion. Another congestion occurs in sections along the Baishazhou Avenue ahead of and behind the Meijishan Interchange, where the heaviest tra ffic, in dark red color, also appears at the 8:00 time node while the sections are in yellow color at the other three time nodes. The rest of the time point is yellow. Compared to the roads in the model, intersections corresponding to the Qingling Interchange are Intersection 10, 12, and 16, while those corresponding to the Meijiahan Interchange are Intersection 35 and 37. It can be seen that most sections of congestion projected by Gaode are in accordance with the congested intersections as simulated in the model. Based on the above analysis, the results of the present model's simulation are consistent with tra ffic forecasts of Gaode.
