APPLICABILITY OF AUTOMATIC LEVEL CROSSING TECHNOLOGY IN INDONESIA
NANDA AHDA IMRON, Dr. Eng. Imam Muthohar, ST., MT.
2013 | Tesis | S2 Mag. S. & T.Transportasi-
Indonesia has many level crossings (LCs) which are all manual with poor performance. Consequently accidents are very often. These accidents are caused by crossing keepers late in lowering the barriers, road users’ recklessness and impatience. The uncertain waiting time at LCs creates significant delays and queues and leads road users breach the lowered barriers. On the other side, manual LC reduces the train speed since train is only permitted to run ≤ 60 km/h on it. Therefore, this study is worthwhile to be conducted. This study has three main objectives; analysing the current LCs’ performance in Indonesia, identifying the Automatic Level Crossing (ALC) technology from international experience and analysing its appicability to Indonesia. Two types of method are proposed to undertake study in LC; primary and secondary data collection. Primary data is a set of data obtained from direct observation on site (survey) such as number of vehicle, delays, queues, barrier time, road users’ behaviour and socio-characteristic of crossing keepers. Once all road traffic data are obtained, the next step will be to generate models; traffic delay and queue models. A regression method will be useful to generate these models. Secondary data is a set of data obtained from previous studies, regulations, and official websites from the Goverment or bodies/institutions. Analysing information from both data source will determine the best choice of ALC for Indonesia. In Jakarta, manual LC creates significant disturbance to road traffic; 158.338 sec of delay and 66.771 m of queue. Other countries such as Great Britain, Japan and Australia have successfully improved their LC by installing ALC. It is not only reduce the disturbance but also improves safety by minimising human errors (crossing keepers). Many types of ALC are available but the most suitable for Indonesia is Crossing Barrier with Obstacle Detection (CBOD). This study revealed that it creates lower disturbance to road traffic by 114.398 sec of delay and 47.751 m of queue. It also increases the line speed up to 90 km/h (considering the maximum train speed). The simulation results that CBOD provides 23.356 sec faster of train’s journey time but it slightly increases the train’s energy consumption by 0.034 kWH. Indeed, trains run with different speed at LC. Therefore, Level Crossing Predictor (LCP) is required to provide relatively constant barrier time (barrier time for CBOD = 50.9 sec while manual LC = 70.9 sec). Overall, CBOD is more reliable than manual LC. Considering the delay and queue reduction, theoritically CBOD can improve manual LC’s performance by 28%. Further, in order to manage road users’ behaviour at LC, traffic calming should be implemented; rumble strips, traffic channelization and countdown timer. This study also revealed that the Benefit Cost Analysis of CBOD installation (includes traffic calming) results in 1.24, showed that it is feasible to be implemented. It should be applied as soon as possible but regarding to its massive number of LC and limited financial support, priority in upgrading LC must be made. The Australian Level Crossing Assessment Model (ALCAM) is the most comprehensive way in arranging its priority. Due to the complexity of LC problems, several actions also need to be taken such as close all illegal LCs, develop the National Level Crossing Database and educate all road users’ segments; including students, public and transport industries to develop safety driving culture.
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