Severe traffic jams during the long Tomb Sweeping Day holiday this month caused public complaints. As people went home to clean their family’s tombs and then perhaps travel further on the first two days of the holiday, congestion was unavoidable.
On the first day, the Ministry of Transportation and Communications urged people traveling distances less than 50km to avoid highways and to take alternative routes instead.
As usual, highway toll adjustments were adopted, with toll-free hours between midnight and 5am during the holiday period, a 20 percent toll discount during the two weekends prior to the holiday and the implementation of the high-occupancy vehicle (HOV) policy, but with very limited effect.
In addition to a concentration of traffic, what other factors lead to severe congestion, and how can the situation be improved?
First, the ministry should review and improve the precision of its predictions of highway traffic distribution and average driving time during the holiday.
By using big data analysis when estimating traffic volumes, several factors can be taken into account, such as the convenience of driving during the tomb sweeping holiday, weather conditions, the willingness of those who did not travel during the Lunar New Year’s holiday because of the Feb. 6 Hualien earthquake to travel during this holiday instead, as well as the customary family trip on Children’s Day.
If the estimate of when and where congestion will occur is inaccurate, preventative measures will not yield the anticipated effect, but rather achieve the opposite.
Examples can be found in the long traffic jams on the Chiang Wei-shui Memorial Freeway (Freeway No. 5) on the first day of the spring holiday: To make the best use of the toll-free period between midnight and 5am and avoid the HOV restriction starting at 7am, too many people embarked on their trips earlier than usual, so congestion started at 4am.
The traffic jams continued and the freeway was severely congested all day. The traffic exiting from Suao Interchange 5 was so heavy that the Suao-Hualien alternative route and the Suao-Hualien Highway were also severely congested all day.
The review and analysis of ways to improve the severe traffic congestion during holidays only includes reducing and rerouting traffic. Traffic reduction strategies require raising the appeal of alternative public transportation.
In addition to giving public transportation vehicles right of way, the government should also improve real-time bus information systems to help the public find seamless transfer transportation to their destination.
It should also provide an integrated ticketing platform and offer package discounts, and public transportation companies should increase dispatching frequencies to avoid overcrowding and deteriorating service quality. Big data analysis should be used to provide accurate traffic information to travelers, issue announcements and alerts, and implement controls as needed.
One week before a holiday, the public should be able to enter the origin and destination of their trip to find information on when and along what routes to drive, or whether use public transportation, so that they can plan their trip.
The day before the trip, users should be able to check updated traffic forecasts, so that people who plan to drive can adjust their departure time or route accordingly.
On the day of travel, the authorities could conduct traffic controls based on real-time conditions, and the public should be able to check the latest information on traffic controls to be able to respond to any changes in the situation so that traffic can be rerouted as necessary.
The ministry is implementing the HOV policy to encourage carpooling to reduce the number of vehicles on the road. However, while this makes traffic flow smoother on the highways, the ministry has overlooked the severity of congestion around on-ramps where manual controls are conducted to make sure that vehicles comply with the HOV policy.
An analysis of the proportion of HOV vehicles on national highways is recommended. If it exceeds 70 percent of the total number, the effectiveness of the policy should be carefully evaluated. If the policy is still necessary, the ministry should rely on technology such as infrared identification instead of manual checks.
The National Freeway Bureau responded by conditionally allowing vehicles to drive on the shoulder during designated periods. However, accidents were another factor contributing to the severe congestion.
Attention should be paid to the fact that opening road shoulders to traffic could delay ambulances and police patrol vehicles handling a situation, which will further aggravate congestion.
The bureau should adjust the width of lanes on parts of the highway where congestion frequently occurs, and widen road shoulders as required.
In the future, road shoulders should only be opened to buses; this will prevent private vehicles from taking advantage of the highway shoulders to change lanes at their convenience, which interferes with traffic flow, and could cause collisions and further traffic congestion.
The ministry should learn from this year’s experience and examine the rerouting and traffic distribution measures used. Big data analysis should be used to evaluate the effectiveness of the various traffic distribution measures and analyze road conditions. Preventative measures should be flexible and easy to adjust.
For instance, when a significant share of highway traffic is not made up of high-occupancy vehicles, the HOV regulation could be applied along with a toll-free period during the night, which would not only encourage carpooling, but also yield the benefit of passengers reminding each other to drive safely.
Traffic controls and management should be conducted while considering the requirement to make transportation smarter and more intelligent, and also taking advantage of environmentally-friendly technologies. With the incorporation of big data and technology, different toll rates could be applied to peak or off-peak periods, as well as to different routes.
Lee Ker-tsung is an associate professor in Feng Chia University’s transportation technology and management department.
Translated by Chang Ho-ming
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