Indonesia and shorten red signals to make sure

Indonesia is
trying to develop its public transportation system for local and tourist use.

Better public transportation may encourage tourist travel, and therefore
tourist spending, which would help the Indonesian economy. However, even with
increased use of public transportation, congestion may not be sufficiently
alleviated. One complementary yet concealed development, so as not to excessively
change the physical landscape of our area of focus, Yogyakarta and its surrounding
municipalities in exchange for increased mobility, is an improved traffic
signal system. Traffic signals, which are also referred to as traffic lights,
traffic lamps, traffic semaphores (antiquated), signal lights, stoplights, and robots
(South Africa), are used to safely manage congestion. Traffic signals have
evolved substantially since their inception in the early 20th
century (Osborne, 2014). In the past, electric traffic signals were timed
(Synthetic Programming, 2016). Now, many traffic signals use sensors located
above the traffic signals themselves or below the road pavement (Neal, 2016). The
sensors are subterranean sensors are inductors, which generate a magnetic
field, and when a car is parked overhead, cars being composed of conductive
material, amplify the magnetic field, which then changes the electric current
of the system; this change is sensed by the traffic signals and allows them to
determine which lane a car is in (Don’t Be Afraid To Learn Something, 2016).

The overhead sensors use infrared technology to communicate with passing
vehicles (Synthetic Programming, 2016). In particular, emergency vehicles,
such as ambulances, have used this technology to emit infrared signals that
indicate the urgency of an emergency, and are given priority in traffic lanes
(Synthetic Programming, 2016). On top of sensory technology, traffic control
is also managed using people, cameras, computers and algorithms (RAAofSA,
2016). Many comprehensive traffic management systems exist today, such as IVHS in
the United States of America and DRIVE in Europe (Aoyama, 1994).

 

In Japan, the National
Policy Agency (NPA), which manages traffic in Japan, approved the Universal
Traffic Management Systems (UTMS) for traffic control in the country; different
individual or combinations of the eight subsystems of UTMS have since been
applied to different prefectures across the country (UTMS, N.d.). Ultrasonic,
image-based and IR sensors are central to appropriate traffic signal response
to local traffic condition. Below are the eight subsystems of UTMS and their
usages, all sourced from the UTMS website. Firstly, Advanced Mobile Information
Systems (AIMS) gives drivers information on traffic congestion and travel time from
a traffic control center to ‘electronic display boards, on-board navigation
systems and other media.’ Secondly, Public transportation priority systems
(PTPS) extend green signals and shorten red signals to make sure buses arrive
on time. Thirdly, Mobile Operating Control Systems (MOCS) facilitate the
tracking of buses and trucks by their respective companies to better manage
their operations. Fourthly, Environment Protection Management Systems (EPMS) uses
‘roadside exhaust centers and noise sensors’ to measure the environmental
impact of motorized vehicles, and ‘uses traffic data gathered by infrared
beacons’ to send drivers ‘information about alternative routes’, which limits congestion
and lowers the overall carbon footprint in the area. Fifthly, Driving Safety
Support Systems (DSSS) ’employs sensors to detect cars, bicycles, and
pedestrians that are difficult for drivers to see,’ and uses infrared beacons
to provide information to the cars navigation systems that can help drivers
avoid potential hazards. Sixthly, Pedestrian Information and Communication
Systems (PICS) help the visually impaired and the elderly through ‘voice
guidance telling pedestrians the names of intersections and the status of
crosswalk signals’, and allowing green light extensions. Seventhly, Fast
Emergency Vehicle Preemption Systems (FAST) ‘extend green lights and shorten
red lights for emergency vehicles, and helps prevent accidents involving the
emergency vehicles themselves.’ Eighthly and finally, Help system for Emergency
Life saving and Public safety (HELP) routes information on traffic accidents
through an operation center, and sends that information ‘to police and other
first responders with details of the accident and the location of the vehicles
involved.’ According to UTMS, from 2003 to 2006, such systems helped lower
traffic fatalities by an estimated 33,000, cut CO2 emissions by
480,000 tons, and reduced total intersection wait-time by approximately 230
million man-hours. If Yogyakarta adopted such systems it could create a safer,
environmental-friendlier, more efficient not only public transportation, but
general transportation system for locals and visitors.

 

       Despite
UTMS being an advanced and comprehensive traffic signal preemption system, it
does not address the issue of multiple conflicting requests on an isolated
intersection; for example, suppose two delayed buses are traveling
perpendicular to each other. Transit Signal Priority Connected Vehicles –
Conflicting Requests (TSPCV-CR) is a traffic control technique that can settle
conflicting requests (Hu, Park & Lee, 2016). Although researchers Xu and
Zheng (2012) found that TSP alone could moderate BRT transit delay with
particular effectiveness over curb instead of median bus-only lanes, Hu, Park
and Lee (2016) highlight how TSP alone operates on a first-come, first-serve
basis, sacrificing one lanes green signal for another. In cooperation with
Connected Vehicles (CV) technology, this limitation can be overcome. Connected
Vehicles technology is wireless communication between ‘vehicle and vehicle,
vehicle and infrastructure, and vehicle and personal device’; it helps
prioritize buses at intersections and arterial thoroughfares (United States
Department of Transportation, N.d.). Rather than extending the green signal,
which would delay perpendicularly oncoming traffic, TSPCV-CR simply reallocates
green signal intervals, as show in the image below.

     

Source:
Hu, Park & Lee, 2016

 

As a result, multiple buses are simultaneously
accommodated. TSPCV-CR is run by a mathematical program known as Binary Mixed
Integer Linear Program (BMILP) (Hu, Park & Lee, 2016). To test the
effectiveness of TSPCV-CR researchers Hu, Park and Lee (2016) used analytical
and small-scale traffic simulations to measure bus delay and total travel time
of passengers under four levels of congestion and three types of conflicting
scenarios and using TSP logic, TSPCV-CR logic or no TSP logic. The four
congestion levels were defined by a volume demand to capacity ratio (v/c) of
0.5, 0.7, 0.9 and 1.0, where v/c = number of vehicles on roadway at a snapshot
in time divided by maximum capacity of roadway (Hu, Park & Lee, 2016; polly
okunieff, 2009). Using TSPCV-CR the researchers found an average bus delay reduction
of 5% to 48% across ascending congestion ratios, which were all higher than
using exclusively TSP or no TSP. TSPCV-CR was essentially more beneficial, the
more congested it became, and no statistically negative effects were observed.

Moreover, they found that TSPCV in the case of single bus delay reduced delays
by up to 57%, which is even more effective than during conflicting requests. In
order to most benefit from such technology, when mapping bus routes, urban
planners should try and avoid intersecting routes, or at least avoid intersecting
time zones for conflicting bus routes.   

The other advantages of green-time is
reallocation include that fact that it is implemented only when a bus is behind
schedule, so that there is no additional total person delay. Moreover, ‘TSPCV-CR
provides more preference to buses traveling on the direction with a higher
volume (major street) under most traffic conditions, but switches its
preference to the minor street under near capacity condition’ (Hu, Park &
Lee, 2016). In addition, the computation time can take under 1 second under
suitable technological conditions, and the logic can be applied to a variety of
intersection geometries (Hu, Park & Lee, 2016). Nonetheless, there are some
issues with TSPCV-CR. Foremost, the program may be inaccurate in its
estimations depending on bus dwell time and roadway volume, with higher volumes
implying higher vulnerability (Hu, Park & Lee, 2016). Additionally, the
program occasionally sacrifices one bus lane to achieve an overall delay reduction
(Hu, Park & Lee, 2016). Nonetheless, researchers Hu, Park & Lee (2016) advocate
potentially 100% bus accommodation, with definite real world and real-time
applications, and a predicted 25% carbon emissions reduction. In an earlier BRT
case study in Changzhou, China, where the researchers used a personal signal
priority scheme, they also found up to 25% emission reduction based on their
microscopic simulations; in particular their study addressed ‘delays caused by
queued vehicles and transit station’, which does not seem to be as in focus in
the TSPCV-CR study (Ji, Hu, Han & Tang, 2014).

 

       In
conclusion to this section on road usage and traffic control systems, with
particular emphasis of traffic signal preemption for congestion management,
there are innovative technologies in place which can be used to alleviate
congestion: notably the Japanese UTMS system or TSPCV-CR. It seems that green
light prioritization is an important traffic control technique, and it can be
used to smoothen public transportation operations, essentially reducing congestion,
reducing delays and reducing carbon emissions. In the context of the Indonesia
project, a more efficient transportation system could be used not only to
improve the lives of local residents, but also facilitate travel for
tourists.