**4. Discussion**

Statistics in the OD matrix of residents' travels at di fferent hours show that, most of the spatial units follow the pattern of minimum tra ffic at 9:00, but there are also a few plots, such as Plot 27, that do not match the major pattern of travel numbers. Situated in the South Lake area and serving mainly residential functions, Plot 27 is one of the most congested areas in Wuhan during rush hours. A possible explanation is that the residents of the area choose to delay their departure time to 9:00 in order to avoid the tra ffic congestion period from 7:00 to 8:00, or they are stalled in tra ffic for too long and are considered as having not departed from the area in the statistics. This result, to some extent, verifies the significance of OD statistics and simulation by each hour: when the travel patterns of each space unit during the four hours change, tra ffic pressure at each intersection may also vary, and the causes behind these changes demand further analysis with the support of simulations. This is also why this study divides the unit time span of commuting travels into a one-hour basis.

In order to test the simulation results of the model for di fferent schemes, and combined with the analysis of the causes of congestion, the roads in the study area are optimized according to the Wuhan master plan. As a measure of optimization, a waterfront north–south road along the Yangtze River and the road to the South Lake area are planned (Figure 6b). The planned and optimized road network is simulated in the model and compared with the original one. In this simulation, it is assumed that the population in this area remains unchanged and so do the places of residence and work. Comparing the simulation results of the two schemes (Figure 9), it can be seen that the optimized scheme is evidently better than the scheme before optimization: first, tra ffic has been distributed to multiple road intersections instead of being concentrated at an intersection before the optimization. Second, duration of tra ffic congestion is significantly shortened, which means congestions can be alleviated quickly even if they do occur.

According to Gaode's Tra ffic Report on major cities in China, 81% of them su ffer from congestions during rush hours of residents' commuting [49]. Therefore, studying residents' commuting behavior as a starting point to address the wider problem of urban tra ffic congestion bears practical significance, not only for China but also for the world at large. The era of big data is coming. When it is less di fficult to acquire data, how to use them in urban research becomes an issue that calls for deliberation [40]. Previous studies prove that mobile phone call data can more accurately reflect the commuting features of urban residents. However, most studies focus on the overall analysis of cities on a macro scale and

the visual representations. Few studies are found on the micro scale dynamic analysis of residents' commuting behaviors, or on how commuting relates urban traffic.

However, as the sole data source used in the present study to understand residents' mobility, CDR data still has limitations because it is a relatively sparse data in recording the travel trajectory of residents. It is difficult to obtain the traffic mode (or speed) of residents' travel through statistical analysis. Thus, the specific correlations between commuter vehicles and mobile phone users are not discussed in the present study. Therefore, in follow-up studies, additional data sources such as bus card and traffic cameras at road intersections may facilitate the cross-examination of our research results or the setting rules of residents' commuting at a finer time-scale. Of course, these rely on the availability of data, which remain difficult to collect compared with other sources at present.

**Figure 9.** Comparison of traffic conditions before and after the optimization of the road network. (**a**) The traffic conditions of road intersections before optimization; (**b**) The traffic conditions of road intersections after optimization.
