This study applied GIS-based statistical analytic techniques to investigate the influence of accident Severity Index (SI) on temporal-spatial patterns of accident hotspots related to the specific time intervals of day and seasons. Road Traffic Accident (RTA) data in 3 years (2015 − 2017) in Hanoi, Vietnam were used to analyze and test this approach. Firstly, the RTA data were divided into four seasons in accordance with Hanoi's weather conditions and the time intervals such as the daytime, nighttime, or peak hours. Then, the Kernel Density Estimation (KDE) method was applied to analyze hotspots according to the time intervals and seasons. Finally, the results were presented by using the comap technique. This study considered both analyses with and without SI. The accident SI measures the seriousness of an accident. The approach method is to give higher weights to the more serious accidents, but not with the extremely high values calculated on a direct rate to the accident expenditures. The results showed that both analyses determined the relatively similar hotspots, but the rankings of some hotspots were quite different due to the integration of SI. It is better to take into account SI in determining RTA hotspots because the gained results are more precise and the rankings of hotspots are more accurate. From there, the traffic authorities can easily understand the causes behind each accident and provide reasonable solutions to solve the most dangerous hotspots in case of limited budget and resources appropriately. This is also the first study about this issue in Vietnam, so the contribution of the article will help the traffic authorities easily solve this problem not only in Hanoi but also in other cities. ARTICLE HISTORY

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Purpose -Urban networks are usually divided into several open or closed sub-networks. Signal coordination has been recognized as one of the most efficient methods of controlling sub-networks that have independently optimized timing plans. However, coordinating adjacent intersections in a network is a basic prerequisite to optimizing signal-timing plans for sub-networks. This paper aims to develop a linear model to support decisions regarding coordination of adjacent signals.Design/methodology/approach -This paper aims to develop a linear model to support decisions regarding coordination of adjacent signals. The tests of this model which using the field data differ from those for calibration from various roadways, indicating that the model has transferability. Evaluations using microscopic simulation show that the model can objectively determine whether or not to interconnect adjacent signals depending on various traffic demands. Findings -The model was calibrated by stepwise regression analysis with a total of 195 field samples. This model consists of the dependent variable critical block length (CL) between adjacent intersections, and the independent variables original platoon size (OPS) and platoon completeness ratio (PCR). The calibrated model is shown as following: CL ¼ 689.97 þ 6.86 OPS 2 7.15 PCR. Originality/value -The proposed model appears to be a viable solution for determining whether to coordinate adjacent signals according to various traffic demands for variously configured roadways. The model shows that a larger OPS or a smaller PCR implies a larger CL. The model also indicates that adjacent signals must be interconnected if they are separated by 690 meters or less. The results also suggest that OPS from 10 to 30 fully disperse at about 800 meters downstream of a stop line. The results support the CL for effectively coordinating adjacent signals, similar to that recommended in the Manual on Uniform Traffic Control Devices. These results may be useful for the effective management of traffic signal networks.