This study uses driving behavior and speed variation data from navigation software to propose a traffic order index (TOI) for evaluating the order of traffic on urban roads. Based on the significance analysis of driving behaviors and speed variation under different road types and congestion levels, the TOI calculation method is proposed by using the order of preference by similarity to ideal solution (TOPSIS) method. Through a case study of an urban area in Beijing, the distribution of TOI under different road types and congestion levels is described, and the hourly and daily TOI heat maps are generated to show changes in TOI for urban roads during different periods. The relationships between TOI, congestion index (CI) and crash data were explored. A nonlinear relationship of TOI and CI was discovered, and roads with more crashes or longer crash durations were associated with a lower level of traffic order. The TOI could help traffic management departments better understand the order of traffic on roads, reveal causes of the poor level of traffic order in some roads, and more reasonably dispatch traffic police to handle traffic crashes.