Researchers Erma Suryani and Rully Agus Hendrawan from the Department of Information Systems, Institut Teknologi Sepuluh Nopember,Surabaya, Indonesia,
Philip Faster Eka Adipraja from the Department of Informatics, STMIK Asia Malang, Malang, Indonesia Arif Wibisono from the Department of Information Systems, Institut Teknologi Sepuluh Nopember,Surabaya, Indonesia, and Lily Puspa Dewi
Department of Informatics, Universitas Kristen Petra, Surabaya, Indonesia published an article in the Journal of Modelling in Management, regarding the urban mobility modeling to reduce traffic. The article provides an up-to-date review of this subject. Here are some key issues.
Traffic congestion occurs when the travel demand exceeds the limited supply of transportation services. Traffic congestion is characterized by lower speeds, longer travel times and increased vehicular queuing.
This study shows the impact of various policies on transportation management that can potentially be implemented to increase urban mobility and reduce traffic congestion.
To accomplish the research objectives, a System Dynamic (SD) model was used because it is a useful tool to support policy analysis and decision-making. The authors also developed a number of scenarios (i.e. MRT development, BRT development and public transport delay reduction) to improve mobility and reduce traffic congestion.
The study was conducted at Surabaya City, East Java, Indonesia, which is the fourth most-congested city in the world. The number of vehicles continues to increase with an average of over 3 per cent annually, while the construction of roads is less than 1 per cent annually. Based on the above problems, the research understands that it is necessary to improve urban mobility and mitigate traffic congestion. Improving mobility can support efforts to achieve sustainability and reduce traffic congestion in urban areas.
Mass rapid transit development scenario
MRT is a modern urban public transport system that moves a large number of people on short to medium length journeys. The MRT development scenario was designed to improve urban mobility and reduce traffic congestion. Challenges in MRT development include land acquisition for the relocation of public utilities, such as the removal of gas pipes, raw water pipes and electricity cables. In developing the MRT project, policies are required in managing the authority to administer railroad facilities and infrastructure.
MRT capacity depends on how many passengers per hour it can be expected to carry. The maximum capacity can be defined as 100 passengers per vehicle, 10 vehicles per train, 30 vehicle sets per hour = 30,000 passengers per hour. Currently, the ADT in Surabaya is around 1.85 million vehicles, and the percentage of private vehicles is about 43 per cent. We assumed that each private vehicle was occupied by one person based on the tendency of people to travel for personal interest purposes, hence the average number of passengers in private vehicles is around 0.43 1.85 million = 795,500 passengers per day, i.e. around 795,500/24 = 33, 146 passengers per hour. The maximum capacity of MRT is 30,000 passengers per hour, hence the maximum absorption capacity of MRT would be able to reduce private car use by = 30,000/33.146 100 per cent = 90 per cent.
Based on the maximum capacity of MRT and the expectation that not all private vehicles users will switch to MRT, as happened after the implementation of light rail transit in Palembang, researchers assumed that there would be around 35-45 per cent of private vehicle users who will switch to MRT.
In all, 35.8 percent of private vehicle users will switch to MRT initially, after which this number will grow at around 1.5 per cent per year.
Bus rapid transit development scenario
BRT is a high-quality bus transit system that can provide services that are fast, convenient and cost-effective. In the construction of BRT developments in Indonesia, a large amount of funding is required, where a 5-km BRT development would cost US$5m. The initial number of buses is estimated around 30 with a capacity of 30 people per vehicle. Hence, the maximum capacity of BRT = 30 30 10 6 = 54,000 passengers
The total volume of daily traffic of all transport modes = 1.85 million passengers and the percentage of private vehicles = 43 per cent, hence the total number of people transported in private vehicles = 0.43 1.85 million = 795,500 passengers.
Therefore, the BRT project will be able to decrease the number of private vehicle users by 54,000/795,000 = 0.068 = 6.8 per cent (or about 7 per cent). This value can be used to determine the initial percentage of private vehicle users that switch to BRT. The percentage of BRT users is projected to grow 2 per cent annually because of changes in user behavior and the addition of buses.
With MRT and BRT development, the percentage of private vehicles can initially be reduced by 50 per cent and it will continue to decrease at around 1.4 per cent per year because of private vehicle users switching to MRT and BRT,. The percentage of ADT is initially reduced by 35.4 percent initially and is predicted to decrease by 21.2% in 2035
Public transport delay reduction scenario
Another strategy to increase urban mobility and mitigate congestion is reducing public transport delay. Travel time delay can be reduced from an average of 24 min to an average of 10 min by a decrease in public transport delay, hence it can increase delay performance by an average of 67.5 per cent to an average of 137 per cent.
The simulation result shows that MRT and BRT development could decrease the average daily traffic (ADT). The ADT after MRT and BRT development is projected to be reduced by 1.3 million vehicles by 2020 and 1.55 million vehicles by 2035. Meanwhile, the urban mobility performance (after the reduction of public transport delay) is projected to increase by a minimum of 50 per cent and a maximum of 70 per cent.
With the increase in urban mobility and the decrease in ADT, traffic congestion is predicted to decrease by a minimum of 57.6 percent and a maximum of 69 per cent. These values indicate that traffic congestion will be reduced under the maximum saturation of 85 per cent.
The percentage of private vehicle users switching to MRT is predicted to be 35.8% initially and then will grow at an average rate of around 1.5% per year.
The percentage of private vehicle users switching to BRT is predicted to be 6.8% initially and then will grow at an average rate of 2% because of changes in user behavior and the addition of buses.
The percentage of private vehicles will be reduced by 50% initially because of private vehicle users switching to MRT and BRT and it will continue to decrease at an average rate of around 1.4% per year. The percentage of ADT is reduced by 35.4% initially. and is predicted to decrease by 21.2% in 2035.
The travel time delay can be reduced from an average of 24 min to an average of 10 min because of the decrease in public transport delay and hence it can increase delay performance from an average of 67.5% to an average of 137%
MRT and BRT development will decrease ADT. The ADT after MRT and BRT development is projected to be reduced to 1.3 million vehicles by 2020 and to 1.55 million vehicles by 2035. Urban mobility performance after MRT and BRT project development and public transport delay reduction is predicted to increase by a minimum of 50% and a maximum of 70%.
With the increase in urban mobility and the decrease in ADT, traffic congestion is predicted to decrease by a minimum of 57.6% and a maximum of 69%.
The authors stress that the novel contributions of this research are: formulating relationships between variables; building the dynamic behavior of urban mobility and traffic congestion; and building scenario models to improve mobility and reduce traffic congestion. The scenarios enabled us to test some alternative policies and observe the overall impact of the proposed solutions by modifying the model structures and parameters.
Further research is required to develop a sustainable transport system by considering economic (e.g. infrastructure, energy, pricing and competitiveness), social (e.g. operations, access, safety and health) and environmental factors (e.g. air quality and land use). and mobility, several strategies can be implemented, such as MRT and BRT development and public transport delay reduction.