Transportation Research Interdisciplinary Perspectives 2 (2019) 100052
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Transportation Research Interdisciplinary Perspectives journal homepage: https://www.journals.elsevier.com/transportation-researchinterdisciplinary-perspectives
Evaluation of traffic management strategies for special events using probe data ⁎
Claude Villiers a, , Long D. Nguyen a, Janusz Zalewski b a b
Department of Environmental and Civil Engineering, U.A. Whitaker College of Engineering, Florida Gulf Coast University, USA Department of Software Engineering, U.A. Whitaker College of Engineering, Florida Gulf Coast University, USA
A R T I C L E
I N F O
Article history: Received 9 January 2019 Received in revised form 28 August 2019 Accepted 1 September 2019 Available online xxxx Keywords: Traffic management Special events Bluetooth sensors Probe data Travel time Signal retiming
A B S T R A C T
Special events can impose burdens to local roads. As these events are temporary and even seasonal in nature, concerned agencies need to identify cost-effective traffic management strategies to control this increased traffic. The current research empirically investigated the traffic flow, traffic volumes, and traffic management strategies for sporting events in Fort Myers, Florida. Extensive data were collected for over five consecutive years on an arterial road. These data contained traffic volumes from available loop detectors and travel time from Bluetooth sensors. Results showed that, like the experience curve effect, manual traffic control seemed to improve the traffic after the first year but leveled off thereafter. Signal retiming was effective for traffic entering games but not after games. The average travel time on a certain road segment for through traffic before the event starts was reduced by >40% after the signal retiming. Variable message signs (VMS), while appeared to help traffic management, might not considerably improve travel time before and after the events on the road investigated. Although an alternative route was introduced for through traffic, most of drivers still used the arterial road even during peak congestion. With an average penetration rate of >4%, this long-term study confirmed that the use of Bluetooth-based systems in collecting traffic probe data are still feasible in the near future. This current study contributes to the traffic management body of knowledge by empirically investigating the traffic management plans used for sporting events with objective and quantitative data in a five-year period.
1. Introduction Special events such as fairs, concerts, festivals, and sporting games increase travel time and can lead to severe congestions. Traffic created by special events can be more difficult to control as these events are temporary and have a large number of participants in a limited infrastructure (Stopczynski et al., n.d.). They in fact have significant negative impacts on traffic flow and safety (Eck and Montag, 2003). Latoski et al. (2003) identified challenges uniquely posed by planned special events: (1) managing intense travel demand, (2) mitigating potential capacity constraints, (3) influencing the utility associated with various travel choices, and (4) accommodating heavy pedestrian flow. In response to such challenges, transportation agencies introduce and implement various traffic management strategies. The transportation agencies also need to evaluate the effectiveness of their plans for continuous improvement in reducing traffic congestion over years. Unfortunately, limited research has been undertaken in
⁎ Corresponding author at: 10501 FGCU Blvd S, Fort Myers, FL 33965, USA. E-mail addresses:
[email protected], (C. Villiers),
[email protected], (L.D. Nguyen),
[email protected]. (J. Zalewski).
http://dx.doi.org/10.1016/j.trip.2019.100052 2590-1982/©2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
the evaluation of the traffic management strategies for special events, especially over a long term period. This study aims at filling this gap by empirically evaluating traffic management strategies for sporting events located along an arterial road in a five-year period (Fig. 1). Because several traffic management strategies were gradually implemented in this five-year period, this study collected and analyzed longitudinal data to evaluate the various such strategies. This study chose travel time as one of the key measures in evaluating traffic management strategies. Because special events cause variability in travel time (Federal Highway Administration (FHWA), 2005), it has increasingly concerned transportation agencies and other stakeholders due to its potential negative effect on traveler frustration (Martchouk et al., 2011). Traffic volumes were collected from loop detectors installed by the Lee County Department of Transportation (Lee DOT) while travel time was determined from Bluetooth sensors (probe data) installed by the research team in collaboration with Lee DOT (Fig. 1). The distances between Bluetooth sensors 1 and 2, 2 and 3, 3 and 4, and 1 and 4 were approximately 2.6 km (1.6 miles), 1 km (0.6 mile), 2.9 km (1.8 miles), and 6.5 km (4 miles), respectively. Although other sensors were set up on the roadway, only the four sensors that collected data for the five-year study period are illustrated in Fig. 1.
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Fig. 1. Study area and sensor locations.
2. Previous research
travel times. Although limited quantifiable data were collected, signal retiming, manual traffic control, and road closures were found to be beneficial (Lassacher et al., 2009). In the recent decade, the deployment of Bluetooth technology for travel time estimation has been increasingly popular (Araghi et al., 2016; Bakula et al., 2012). Araghi et al. (2016) noted that an increasing number of Bluetooth-enabled devices among motorists, the anonymity of Bluetooth devices, the ease of deployment and maintenance of Bluetooth sensors, and an acceptable level of accuracy prove the potential of this technology for traffic management. In fact, this technology is among the least expensive sources of data and has the potential for providing rich information for traffic monitoring (Bhaskar and Chung, 2013). In addition to providing direct measures of travel time, Bluetooth technology has been used for origin-destination (OD) estimation, route choice modeling, and mode detection (Lee et al., 2016). As such, traffic monitoring and traffic state estimation based on these probe data were used in the contexts of freeways and urban networks (Bucknell and Herrera, 2014). Pesti and Brydia (2017) found that the Bluetooth-based post event closure analysis was very cost-effective based on three-year experience with the analysis of impacts of construction projects on a freeway in Texas. Bluetooth is a standard communication technology that uses a unique electronic identifier called the Media Access Control (MAC) address in each electronic device (e.g., cell phones). With the uniqueness of the MAC address, a Bluetooth sensor can track Bluetooth-enabled devices on vehicles at an upstream point on the road and re-identify these devices at a downstream point. The travel time can be estimated based on the time difference between the two observations (Araghi et al., 2015). The detailed procedure to estimate travel time using Bluetooth sensors can be found in previous studies (e.g., Haghani et al., 2010). Araghi et al. (2015) confirmed that a sensor would on average detect Bluetooth-enabled devices 80% of the time while passing the sensor location. Bluetooth enabled or discoverable devices were found to be more often used by the population than GPS enabled devices (Friesen and McLeod, 2015). Low sampling rates are a major concern of this technology. The penetration rate (or sampling rate) is the percentage of the actual traffic captured by a Bluetooth sensor. For example, the sampling rates in the U.S. vary from study to study, in a low range between 2.0% and 3.4% (Sharifi et al., 2010) and in a higher range between 5% and 7% (e.g., Carpenter et al., 2012). Nevertheless, Bluetooth data are potentially a reliable source for traffic monitoring (Haghani et al., 2010; Laharotte et al., 2015). Bachmann et al. (2013) concluded that the Bluetooth-based traffic
Several methods have been discussed in literature to improve traffic conditions. Intelligent transportation systems (ITS) have been employed to operate and manage current transportation networks (Antoniou et al., 2011). While increasing the roadway capacity is expensive, the implementation of the advanced technologies may improve the efficiency of the existing transportation system and alleviate traffic congestion (Haghani et al., 2010). Hamilton et al. (2013) confirmed that important advances in vehicle detection and communication technologies have enabled a series of step changes in the capabilities of urban traffic control systems. Advanced technologies can be equally applied to this research context that involved the management of traffic for sporting events, especially in expensive technologies to collect travel time data. As a valuable statistic in transportation systems, travel time information supports a wide body of decision makers, from agencies who use this information for monitoring and planning purposes and for emergency responses to motorists seeking to reducing their trip times (Aliari and Haghani, 2012; Feng et al., 2014). The measures of traffic performance include volume, flows, speed, density, travel time, link capacity, and critical occupancy. Rao and Rao (2015) found that various studies used only one or two measures of traffic performance. Kim et al. (2013) used travel time as a major key performance indicator to evaluate weather-responsive traffic management strategies in improving the overall mobility of the network. They found that snow and incidents increased the travel time variability rather than the average travel time. Many other studies have also used travel time information in evaluating traffic management strategies (Al-Deek et al., 2009; Dowling et al., 2011; Shi et al., 2009). Several studies were conducted in the traffic management of special events. Eck and Montag (2003) studied the traffic characteristics of fairs and festivals on low-volume roads in West Virginia, USA. The authors used personal interviews, telephone conversations, and mail questionnaires of event organizers as well as automated and manual traffic counts. Their study did not provide any assessment of the traffic management plans used by local traffic agencies and event organizers. Lassacher et al. (2009) reported a variety of traffic management strategies to manage congestion resulting from football games at Montana State University in Bozeman, Montana, USA in fall 2007. Closed-circuit television (CCTV), variable message signs (VMS), and highway advisory radio (HAR) were the ITS used. The authors conducted average car travel time studies to determine what impact the traffic management strategies (i.e., road closures) had on overall 2
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could balance each other, resulting in higher accuracy of the average travel time.
monitoring system is more accurate than loop detectors on average and suggested that accuracy will be enhanced further with more advanced sensors. To summarize, the evaluation of the traffic management strategies for special events over several years has not been extensively reported in literature. This current study contributes to the traffic management body of knowledge by empirically investigating traffic management plans used for sporting events with objective and quantitative data in a five-year period.
4. Methods The quantitative evaluation of the traffic management strategies requires objective measures. This study used the following measures to assess the traffic impact of special events and the effectiveness of changes in traffic management plans.
3. Contextual background 4.1. Travel time (TT) In 2012, the new baseball spring training ballpark (hereafter called ballpark) with a capacity of nearly 11,000 fans for the Boston Red Sox opened in Fort Myers, Florida, USA. This facility is located along Daniels Parkway (hereafter called Daniels), a major arterial roadway in Lee County, Florida, USA (Fig. 1). The spring training has taken place in late February/early March to late March/early April. Table 1 shows the numbers of afternoon games (AGs) and evening games (EGs) as well as the average attendance. The average attendance per event has gradually increased since 2012 (Table 1). The traffic due to spring training has impacted residents and businesses within the vicinity of the ballpark. Various stakeholders including park officials, the police department, and department of transportation have faced a daunting task of providing traffic control and traffic management during ball games. For the traffic control at this new ballpark in spring 2012, the county incurred a cost of $50,000, which was more than double compared to that at the former ballpark. Prior to the ballpark opening, this road was expanded from two to three lanes of traffic in each direction. Although this expansion was necessary to handle new ballpark traffic, the roadway improvements alone were not sufficient to solve the traffic congestion. Since the inaugural year of this ballpark (i.e., 2012), there has been an urgent need to identify cost-effective strategies to improve traffic conditions during the baseball spring training. The research aimed at identifying and evaluating major traffic management strategies implemented for these games over a period of five years. Previous studies (e.g., Laharotte et al., 2015) raised the issues of the penetration rate and detection quality in Bluetooth data reliability and processing. As Bluetooth sensors sample only a fraction of the vehicles in a traffic flow (Haghani et al., 2010), the Bluetooth penetration rate (or sampling rate) is defined as the percentage of the actual traffic captured by these sensors. With the data collection of the five-year period, this study aimed to see if the use of Bluetooth-based systems in collecting traffic data are still feasible in a foreseeable future although newer cell phones have defaulted to “non-discoverable” mode, which may negatively affect data collection capability. For four weekdays randomly selected in March of 2013–2016, the average penetration rates were 4.6% (March 19, 2013), 4.4% (March 17, 2014), 4.5% (March 26, 2015), and 6.1% (March 17, 2016). The penetration rates were relatively consistent over the four-year period evaluated. These penetration rates were comparable with previous studies. Sharifi et al. (2010) found that the average Bluetooth hourly penetration rate was from 2.0% to 3.4%. In most recent studies, the penetration rate with Bluetooth data collected from a busy urban road was 5% in Istanbul, Turkey (Erkan and Hastemoglu, 2016) and 7% in Chennai, India (Remias et al., 2017). Herrera et al. (2010) indicated that a penetration of 2–3% was sufficient to provide accurate measurement of the traffic flow speed. In terms of Bluetooth data reliability, Bhaskar and Chung (2013) observed that the temporal error in the travel time estimation from different vehicles
As one of the most critical measures of transportation system performance (Martchouk et al., 2011), the travel time was determined in this study from data collected by Bluetooth sensors. 4.2. Travel time change (TTC) Travel time change (TTC) is the percentage change in travel time between traffic on game days (GDs) and non-game days (NGDs) in the same time of days (Eq. (1)). Specifically, TTC ¼
Descriptiona
2012
2013
2014
2015
2016
3 4 9326
5 3 9519
4 4 9716
9 2 9793
14 3 9821
a b
ð1Þ
4.3. Impacted period The time period that the traffic flow was substantially impacted by special events. The traffic is considered impacted when travel time change is equal to or >50% (i.e., TTC ≥ 50%). This is similar to Meese and Pu (2011) who used an industry rule of thumb that congestions are considered to occur when the travel time change is not <50% of the free-flow travel time. It should be noted that weather conditions are very stable and comfortable in Fort Myers, Florida during spring. The total precipitation at the nearest station (Southwest Florida International Airport) in March was 10.4 mm (2012), 12.7 mm (2013), 79.2 mm (2014), 60.7 mm (2015) and 24.4 mm (2016) (National Oceanic and Atmospheric Administration, 2019). These rainfalls occurred in 3 days (March 2015), 4 days (March 2012), 5 days (March 2013 and 2016) and 6 days (March 2014). From hourly precipitation data at the same station (Iowa Environment Mesonet (IEM), 2019), most of these rains were light (i.e., the precipitation rate of 2.5 mm per hour or less) and occurred on non-game days or not in hours of traffic before or after games. Throughout the study period, no major construction occurred on the arterial road. Therefore, such external factors had a minimal impact on the travel time. Both Eastbound (EB) and Westbound (WB) traffic on Daniels Parkway were used for the evaluation. This study focused the following segments (Fig. 1): • Segment 1-2 (2.6 km): This EB segment was from Bluetooth sensor 1 to Bluetooth sensor 2. This segment was used to assess the traffic entering games. • Segment 1-3 (3.5 km): This EB segment was from Bluetooth sensor 1 to Bluetooth sensor 3. This segment was used to assess the through EB traffic that passed the ballpark. • Segment 2-1 (2.6 km): This WB segment was from Bluetooth sensor 2 to Bluetooth sensor 1. This segment was used to assess the traffic exiting games. • Segment 3-1 (3.5 km): This WB segment was from Bluetooth sensor 3 to Bluetooth sensor 1. This segment was used to assess the through WB traffic that passed the ballpark.
Table 1 Spring training and attendance.
Number of AGs Number of EGs Average attendanceb
TT on GDs−TT on NGDs 100% TT on NGDs
Two major sources of data were used for this five-year study period. Traffic count data were obtained from the local county department of transportation. These data were automatically collected by a permanent count station on Daniels Parkway near the ballpark (Fig. 1). The 2012 traffic
AGs = Afternoon games; EGs = Evening games. Source: Spring training, JetBlue park (2019). 3
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of Daniels and Treeline were changed to provide additional green time to the eastbound (from 79 to 104 s) and westbound (from 75 to 104 s) traffic flow. In addition, two cameras were placed at both entrances (one at each entrance). These cameras provided a 360o panoramic view within the vicinity of the ballpark. Lee DOT was able to make business-enabling decisions from a push of a button at their traffic center.
counts were not available (due to roadway construction just completed) for the road under study. The second source of data was from Bluetooth sensors that were set up by the researchers during the game seasons of 2012–2016. BluSTATS software of Traffax, Inc. (Traffax, 2012) was employed to determine the average travel time in 15-minute intervals from these probe data. 5. Results and discussion
5.2. Evaluation of eastbound traffic 5.1. Traffic management strategies Figs. 2 and 3 display the EB traffic volumes on afternoon game days (AGDs) and evening game days (EGDs), respectively in 2013–2016. On NGDs, EB had the highest traffic volume between the hours of 16:00 and 18:00 and reached 1400–1800 vehicles per hour at 17:00 h in March. On AGDs (Fig. 2), the traffic flow peak was around noon, which had 600–1000 more vehicles per hour (50–140% increase) in comparison with NGDs. On EGDs (Fig. 3), EB had the highest volumes between the hours of 17:00 and 18:00, which were concurrent with the usual traffic rush hours on NGDs. The EB traffic flow increased 1000–1200 vehicles per hour (65–90% increase) during the rush hours on EGDs compared to NGDs. The EB traffic only increased in the 2–3 hour period prior to the games' start. Fig. 4 presents the average travel time in 15-minute intervals in segment 1-2 on AGDs between the hours of 11:00 and 14:00 during the five-year study period. It should be noted that AGs started the hours of 13:35 in 2012 and 2013 and in 13:05 in 2014–2016. The average travel time in segment 1-2 in 2012 was the highest. The highest average travel time was approximately 12 min and lasted for an hour before games started in 2012. The impacted period due to games was also longer in 2012, during 2 h prior to the games' starting times (13:35). The traffic congestion decreased in 2013 as the highest average travel time was about 6 min. The impacted period due to games was also less compared to 2012. The traffic in 2014–2016 was also better than that in 2012 in terms of both average travel time and impacted period caused by the games. While there were no significant changes in traffic management plans between 2012 and 2013, the traffic improvement in the following years compared to 2012 (the inaugural year of the ballpark) can be explained by the “experience curve” effect for both onsite traffic control officers and motorists. Onsite traffic control officers (e.g., police) in 2013 or thereafter learned from 2012 to better manually control traffic. Motorists became familiar with traffic patterns during the game times after they were experienced in the first year (2012). Noticeably, there was no considerable traffic improvement during the period of 2013–2016. This indicated that new traffic plans introduced in 2014 (i.e., VMS, earlier AG start time) and in 2016 (i.e., longer signal timings) might not improve the travel time in segment 1-2, where most of motorists entering the games were affected. Similar patterns were also observed in this segment on EGDs. The average travel time on AGDs for through EB traffic (i.e., segment 13) is presented in Fig. 5. The 2012 traffic was again the worst. The traffic statuses in 2013–2015 were very similar. Therefore, the introduction of VMS in 2014 seemed not considerably to improve through traffic. This was because the traffic instructions during games were not new to most of the motorists who were local and routinely used the road over years. Motorists could anticipate which lanes were through traffic or were designated for games before entering this segment. The 2016 EB through traffic significantly improved compared to those in 2013–2015. The 2016 highest average travel time was 5.5 min, compared to 7.7 min (2013), 7.5 min (2014), and 8.3 min (2015) (Fig. 5). The average travel time peak on AGDs was reduced >40% in 2016 compared to the previous three years. This indicated that the changes in signal timings for the intersection of Daniels and Treeline by providing additional green time to EB and WB traffic flows effectively improved the EB through traffic. Similar traffic improvement was also observed in 2016 EGDs. Table 3 provides travel time changes for this segment (1-3) between GDs (i.e., AGDs or EGDs) and NGDs for training seasons 2012–2016. The highest travel time changes in 2016 were 87% before afternoon games and 78% before evening games. In contrast, the highest travel time changes in 2012,
Various traffic management strategies have been used before and after the games. These strategies were initiated by the park officials, police department, Lee DOT, as well as this research team. During this five-year study period, traffic cones and tapers were used to guide traffic on Daniels. The strategies were varied before and after the games. Before each game, EB lane 1 (EB1, close to median) was redirected as left turn movements only to enter the stadium. EB lane 2 (EB2, middle lane) was used for dual movements, left turn traffic to enter the West entrance or through traffic to the East entrance. EB lane 3 (EB3, close to the shoulder) was then designated, especially at the approach of the entrance of the ballpark, as “thru traffic lane” (motorists not attending the games). Traffic was stopped at intervals on the three WB lanes to allow the fans to enter the ballpark on both east and west entrances. When the parking lot on the west main entrance was full, EB1 was closed for left turn movement, and drivers were re-directed to the East entrance using EB2. Once traffic resumed to normal flow (on average 30 min to after the start of the games), the cones were removed on both sides of the roadway. About 1 h prior to the end of each game, traffic cones were placed on Daniels WB at about 305 m (1000 ft) on both east and west entrances. The road was tapered to merge traffic flow from three lanes to WB lane 1 (WB1, close to median). Through traffic on Daniels westbound was accessible only on WB1. WB lane 2 (WB2, middle lane) and WB lane 3 (WB3, close to the shoulder) were designated exclusively for fans leaving the stadium. Only right turn movement was permitted on these two lanes. In addition to the general traffic plans above, Table 2 summarizes plans that were changed over years. In 2012–2016, during the time period of the exiting game traffic (2–4 h after the game start), signal timing for the intersection of Daniels and Treeline Avenue (hereafter called Treeline) was switched to provide additional green time to the eastbound (from 79 to 92 s) and westbound (from 75 to 112 s) traffic flow. This intersection is about 3.2 km west of the ballpark (Fig. 1). This signal retiming was manually turned on and off in the conclusion of the game based on observations from CCTV cameras. From 2014, three electronic variable message signs (VMS) were utilized to communicate with motorists about traffic entering the stadium, specific lanes for through traffic, and alternative routes (Fig. 1). VMS are currently the major means of conveying network information to the road users (Hamilton et al., 2013). An alternative route to the existing road (Chamberlin Parkway, Fig. 1) was also introduced in 2014. In 2016, 1.5 h prior to the game start, signal timings for the intersection
Table 2 Traffic management plans over time. Year
Traffic management strategies
2012–2016 • During the time period of the exiting game traffic (2–4 h after the game start), signal timings for the intersection of Daniels and Treeline were changed to provide additional green time to the EB (from 79 to 92 s) and WB (from 75 to 112 s) traffic flow. 2014–2016 • Three electronic variable message signs (VMS) were utilized to communicate with motorists about traffic entering the stadium, specific lanes for thru traffic, and alternative route. • An alternative route to the existing road was introduced (Chamberlin). • Afternoon games moved 30 min earlier, from 13:35 to 13:05. 2016 • During 1.5 h prior to the game start, signal timings for the intersection of Daniels and Treeline were changed to provide additional green time to the EB (from 79 to 104 s) and WB (from 75 to 104 s) traffic flow. • Evening games moved 1 h earlier, from 19:05 to 18:05.
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Fig. 2. Eastbound traffic counts on afternoon game days.
TTC ≥ 50% in Table 1) was also observed in 2016 for both AGs and EGs. This confirmed the effectiveness of the 2016 signal retiming in the improvement of the EB through traffic before the game start. Both AGDs and EGDs
2013, 2014, and 2015 before afternoon games were 191%, 165%, 168% and 188%, respectively and before evening games were 108%, 142%, 203%, and 125%, respectively. The shorter impacted period (i.e., when
Fig. 3. Eastbound traffic counts on evening game days.
Fig. 4. Eastbound segment 1-2 on afternoon game days. 5
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Fig. 5. Eastbound segment 1-3 on afternoon game days.
5.3. Evaluation of westbound traffic
Table 3 Travel time change (%) of the eastbound segment 1-3. Hours from game starta
Afternoon game days 2012
2013
2014
2015
2016
2012
2013
2014
2015
2016
−2.00 −1.75 −1.50 −1.25 −1.00 −0.75 −0.50 −0.25 Game start 0.25 0.50
25 38 68 96 110 178 191 131 20 −6 −3
35 34 72 78 127 165 122 87 38 14 −1
76 59 96 168 132 144 162 113 29 −5 −4
10 16 48 116 149 188 139 139 30 −2 3
10 19 55 49 75 79 87 45 7 5 −2
7 21 25 49 48 78 108 98 100 120 91
11 18 33 57 87 142 114 118 102 38 5
34 43 52 96 124 166 203 102 27 7 1
15 26 39 56 53 72 125 84 39 2 −2
13 20 32 40 56 73 78 40 31 4 5
a
The WB traffic volumes on NGDs, AGDs and EGDs were also assessed in the period of 2013–2016. On NGDs, WB traffic volumes had two peaks, which the highest was in 7:00 h (an average flow of 1900–2100 vehicles per hour) and the second and lower peak was in 17:00 h (an average flow of 1400–1600 vehicles per hour) in March. On AGDs, the traffic flow peak was around 2–3 h after the game start (in 15:00 and 16:00 h) and had an additional 1200–1400 vehicles per hour (70–110% increase) in comparison with NGDs. On EGDs, WB traffic also peaked around 2–3 h after the game start. Compared to NGDs, the WB traffic flow increased by 1500–2300 vehicles per hour (260–590% increase) approximately 2 h after the game start on EGDs. In general, the WB flow was only increased by game exiting traffic. Figs. 6 and 7 present the average travel time in 15-minute intervals in segments 2-1 and 3-1, respectively on AGDs between the hours of 11:00 and 18:00 during the five-year study period. Over these years, the average travel time and the impacted period after games were similar for both motorists exiting games (segment 2-1) and through traffic (segment 3-1). In addition, the travel time changes and impacted period for segment 3-1 between GDs and NGDs for training seasons 2012–2016 did not improve over the five years (Table 4). This showed that various traffic improvement
Evening game days
Negative hours meant before game start.
in 2016 caused the least traffic delays in terms of the impacted period and travel time change compared to their counterparts in the previous four years (2012–2015).
Fig. 6. Westbound segment 2-1 on afternoon game days. 6
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Fig. 7. Westbound segment 3-1 on afternoon game days.
recommended that traffic controllers find effective and safe methods to block the through WB traffic on Daniels (i.e., WB1) to only allow drivers to use the alternative route after the games. This option may help law enforcement to clear game-exiting traffic more rapidly and to reduce the travel time for WB traffic, which did not significantly improve despite of various strategies introduced in the five-year study period. Park officials should look for better parking management in the ballpark to help improve the WB traffic. Finally, the use of traffic simulations is recommended to analyze the traffic conditions and to evaluate and obtain best strategies for continuous improvement of traffic flow in the area. In summary, this study demonstrates the use of traffic data from Bluetooth sensors and other devices (e.g., automatic traffic counts) to assess the traffic impact of special events and respective traffic management strategies over the years. Bluetooth technology is substantially less expensive than most sensor-based travel time detection alternatives (Singer et al., 2013). Through this case study, the research provides a cost effective approach for transportation agencies in utilizing traffic data from various sources to quantitatively evaluate their traffic plans over a long-term period. The evaluation helps these agencies in identifying initiatives for their continuous improvement in traffic management. Signal retiming was effective for traffic entering games. VMS are typically useful to disseminate traffic information to motorists and may help reduce traffic congestions. However, it was inconclusive to demonstrate the effectiveness of VMS in a quantitative manner in this study. No consistent drop in travel time change was observed over the years due to VMS. It may be because the travel distance (e.g., approximately 1.5 km from VMS A to VMS B, Fig. 1) was too short to observe substantial changes in travel time. As the current technology and software platform allow to process data from Bluetooth sensors and report travel time in real time, agencies may utilize this application to improve traffic conditions. Specifically, the real-time travel time can be (1) displayed on VMS to efficiently direct or reroute the traffic and (2) accessed by and/or reported to motorists through their phones, texts, and emails to help them identify better travel plans. Though the effectiveness was not evaluated, the latter was tried in this study with a very limited number of local motorists. The use of the Bluetooth sensors is an effective approach in collecting travel time data for the evaluation of traffic management solutions. The cost savings are substantial especially when transportation agencies evaluate their traffic management plans over a long period of time. The travel time data collected from the Bluetooth sensors can also be more objective and representative than those collected by the traditional methods. For
strategies (e.g., VMS in 2014, signal timing changes in 2016) appeared not much effective in improving the WB traffic after the games. As mentioned earlier, an alternative route to the existing road (Chamberlin Parkway) was open from 2014 spring training for WB traffic. Drivers on Daniels WB had an option to turn left on the traffic signal at the intersection of Daniels and Gateway Boulevard (hereafter called Gateway) to avoid traffic congestion near the ballpark (Fig. 1). This alternative route was 2.6 km (1.6 miles) longer than the through traffic option. Motorists were encouraged but were not forced to use this alternate route. In addition, no modification was made on traffic signals at Daniels/Gateway and Daniels/Chamberlin intersections for the alternate route. VMS “C” was placed about a 1.6 km (1 mile) east of the Daniels/Gateway intersection (Fig. 1). The VMS was to inform drivers about the alternative route option. It was observed that the number of drivers who selected the alternative route was relatively small. It typically took 8 min for travelers who used the alternative route. Drivers lost 3 min at the two traffic signals of the Daniels/ Gateway and Daniels/Chamberlin intersections. Although it may not be beneficial for drivers to select the alternative route for most of the time, drivers could save a few minutes with the alternative route when the traffic volume peaked, which occurred at the conclusion of the AGs. However, most of the drivers used the regular path along Daniels Parkway. The alternative route can become more effective, especially during the peak hours, if the traffic signals at Daniels/Gateway and Daniels/Chamberlin intersections are appropriately retimed. In addition, the real-time data of travel time should be displayed on VMS devices for future road users to make an informed decision about whether to choose the alternative route or the through traffic on Daniels WB. It is also
Table 4 Travel time change (%) of the westbound segment 3-1. Hours from game start 2.00 2.25 2.50 2.75 3.00 3.25 3.50 3.75 4.00
Afternoon game days
Evening game days
2012
2013
2014
2015
2016
2012
2013
2014
2015
2016
16 55 78 95 179 241 276 276 144
55 154 228 167 154 127 197 197 176
20 57 148 295 371 410 332 199 55
58 107 108 124 191 187 131 113 76
47 115 164 265 231 236 211 151 73
−5 64 138 82 47 52 70 71 22
52 144 225 222 233 178 80 36 110
51 58 109 100 127 251 168 136 8
14 67 102 209 132 177 173 −9 26
131 86 115 60 72 271 232 269 −19
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References
the traffic management of special events, previous studies used personal interviews, telephone conversations, and mail questionnaires of event organizers (Eck and Montag, 2003) or average car travel time studies (Laharotte et al., 2015).
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6. Conclusions This study investigated and evaluated traffic management strategies for special sporting events at a new ballpark from its inaugural year (2012) to 2016. Extensive data were collected for a five-year period, including travel time from Bluetooth probe data and traffic volume from loop detectors. The data were collected during game and non-game days on both directions of the arterial road where the new ballpark is located. Different traffic management strategies were put in place by responsible agencies over the entire time period. Using such quantitative measures as travel time, travel time change, and impacted period, the results showed that the effectiveness of the traffic management strategies varied by themselves and by directions (i.e., EB and WB). Manual traffic control seemed to improve the traffic, especially the EB, after the first year but seemed not to improve more thereafter. Signal retiming (e.g., additional green time provided for this arterial road) was effective for traffic entering games (EB) but not exiting games (WB). That is, the highest average travel time of the EB through traffic on AGDs after the signal retiming was reduced >40%. VMS that showed through traffic lanes and lanes blocked for the events appeared not to significantly improve the travel time on both EB (before the events) and WB (after the events). An alternative route was also introduced from the third year of the ball park with a goal of improving the WB traffic. However, due to its longer distance (2.6 km) most of through traffic drivers still used the WB arterial road even during the conclusion of the games. It is recommended that the real-time travel times of both routes should be displayed and constantly updated on VMS for motorists to choose the alternative route or not. In addition, to ameliorate both through and game exiting traffic after the games, the through WB lane should be blocked to direct the through traffic to the alternative route only. This long-term study confirms that the use of Bluetooth-based systems in collecting traffic data is still feasible in a foreseeable future. The penetration rates were consistent in the four consecutive years with an average of >4%. Although the findings were derived from specific locations and events, the research implications as well as methodologies employed in this research can be duplicated to other locations and events. This study contributes to literature with the empirical evaluation of traffic management plans implemented in special events. The Bluetooth-based system was demonstrated as a cost-effective method to collect traffic data for the evaluation of traffic management strategies. Additionally, these systems will generate big data if deployed over a long period of time. An efficient method for analyzing these big data and visualizing useful and real-time traffic information is necessary for local transportation agencies. This study has several limitations. Although many external factors including weather, traffic incidents during sport days, and penetration rate changes of Bluetooth sensors were similar in the study period, they might have some impact on the travel time variability. They however had less impact on the average travel time, which was determined from the same hour of many days. Weather and incidents did not substantially increase the mean travel time, as reported in Kim et al. (2013). The use of other traffic performance measures such as flows, density and level of service could also provide more robust evaluation results. Acknowledgements The authors thank Mr. Gregory Coggins and Mr. Stephen Jansen from the Lee DOT for support and allowing us to work on this study. The authors also thank Mr. Peter Carnes and Dr. Donna Nelson from Traffax Inc. for technical support with Bluetooth sensors and BluSTATS software. Without their involvement, the present study could not have been completed. 8
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