Estimating Uncertainty of Bus Arrival Times and Passenger Occupancies

Travel time reliability and the availability of seating and boarding space are important indicators of bus service quality and strongly influence users’ satisfaction and attitudes towards bus transit systems. With Automated Vehicle Location (AVL) and Automated Passenger Counter (APC) units becoming common on buses, some agencies have begun to provide real-time bus location and passenger occupancy information as a means to improve perceived transit reliability. Travel time prediction models have also been established based on AVL and APC data. However, existing travel time prediction models fail to provide an indication of the uncertainty associated with these estimates. This can cause a false sense of precision, which can lead to experiences associated with unreliable service. Furthermore, no existing models are available to predict individual bus occupancies at downstream stops to help travelers understand if there will be space available to board. The purpose of this project was to develop modeling frameworks to predict travel times (and associated uncertainties) as well as individual bus passenger occupancies. For travel times, accelerated failure-time survival models were used to predict the entire distribution of travel times expected. The survival models were found to be just as accurate as models developed using traditional linear regression techniques. However, the survival models were found to have smaller variances associated with predictions. For passenger occupancies, linear and count regression models were compared. The linear regression models were found to outperform count regression models, perhaps due to the additive nature of the passenger boarding process. Various modeling frameworks were tested and the best frameworks were identified for predictions at near stops (within five stops downstream) and far stops (further than eight stops). Overall, these results can be integrated into existing real-time transit information systems to improve the quality of information provided to passengers

Examining Route Diversion And Multiple Ramp Metering Strategies For Reducing Real-time Crash Risk On Urban Freeways

Recent research at the University of Central Florida addressing crashes on Interstate-4 in Orlando, Florida has led to the creation of new statistical models capable of calculating the crash risk on the freeway (Abdel-Aty et al., 2004; 2005, Pande and Abdel-Aty, 2006). These models yield the rear-end and lane-change crash risk along the freeway in real-time by using static information at various locations along the freeway as well as real-time traffic data that is obtained from the roadway. Because these models use the real-time traffic data, they are capable of calculating the respective crash risk values as the traffic flow changes along the freeway. The purpose of this study is to examine the potential of two Intelligent Transportation System strategies for reducing the crash risk along the freeway by changing the traffic flow parameters. The two ITS measures that are examined in this research are route diversion and ramp metering. Route diversion serves to change the traffic flow by keeping some vehicles from entering the freeway at one location and diverting them to another location where they may be more efficiently inserted into the freeway traffic stream. Ramp metering alters the traffic flow by delaying vehicles at the freeway on-ramps and only allowing a certain number of vehicles to enter at a time. The two strategies were tested by simulating a 36.25 mile section of the Interstate-4 network in the PARAMICS micro-simulation software. Various implementations of route diversion and ramp metering were then tested to determine not only the effects of each strategy but also how to best apply them to an urban freeway. Route diversion was found to decrease the overall rear-end and lane-change crash risk along the network at free-flow conditions to low levels of congestion. On average, the two crash risk measures were found to be reduced between the location where vehicles were diverted and the location where they were reinserted back into the network. However, a crash migration phenomenon was observed at higher levels of congestion as the crash risk would be greatly increased at the location where vehicles were reinserted back onto the network. Ramp metering in the downtown area was found to be beneficial during heavy congestion. Both coordinated and uncoordinated metering algorithms showed the potential to significantly decrease the crash risk at a network wide level. When the network is loaded with 100 percent of the vehicles the uncoordinated strategy performed the best at reducing the rear-end and lane-change crash risk values. The coordinated strategy was found to perform the best from a safety and operational perspective at moderate levels of congestion. Ramp metering also showed the potential for crash migration so care must be taken when implementing this strategy to ensure that drivers at certain locations are not put at unnecessary risk. When ramp metering is applied to the entire freeway network both the rear-end and lane-change crash risk is decreased further. ALINEA is found to be the best network-wide strategy at the 100 percent loading case while a combination of Zone and ALINEA provides the best safety results at the 90 percent loading case. It should also be noted that both route diversion and ramp metering were found to increase the overall network travel time. However, the best route diversion and ramp metering strategies were selected to ensure that the operational capabilities of the network were not sacrificed in order to increase the safety along the freeway. This was done by setting the maximum allowable travel time increase at 5% for any of the ITS strategies considered

On the continuum approximation of the on-and-off signal control on dynamic traffic networks

In the modeling of traffic networks, a signalized junction is typically treated using a binary variable to model the on-and-off nature of signal operation. While accurate, the use of binary variables can cause problems when studying large networks with many intersections. Instead, the signal control can be approximated through a continuum approach where the on-and-off control variable is replaced by a continuous priority parameter. Advantages of such approximation include elimination of the need for binary variables, lower time resolution requirements, and more flexibility and robustness in a decision environment. It also resolves the issue of discontinuous travel time functions arising from the context of dynamic traffic assignment. Despite these advantages in application, it is not clear from a theoretical point of view how accurate is such continuum approach; i.e., to what extent is this a valid approximation for the on-and-off case. The goal of this paper is to answer these basic research questions and provide further guidance for the application of such continuum signal model. In particular, by employing the Lighthill-Whitham-Richards model (Lighthill and Whitham, 1955; Richards, 1956) on a traffic network, we investigate the convergence of the on-and-off signal model to the continuum model in regimes of diminishing signal cycles. We also provide numerical analyses on the continuum approximation error when the signal cycles are not infinitesimal. As we explain, such convergence results and error estimates depend on the type of fundamental diagram assumed and whether or not vehicle spillback occurs to the signalized intersection in question. Finally, a traffic signal optimization problem is presented and solved which illustrates the unique advantages of applying the continuum signal model instead of the on-and-off model

Data-driven linear decision rule approach for distributionally robust optimization of on-line signal control

We propose a two-stage, on-line signal control strategy for dynamic networks using a linear decision rule (LDR) approach and a distributionally robust optimization (DRO) technique. The first (off-line) stage formulates a LDR that maps real-time traffic data to optimal signal control policies. A DRO problem is solved to optimize the on-line performance of the LDR in the presence of uncertainties associated with the observed traffic states and ambiguity in their underlying distribution functions. We employ a data-driven calibration of the uncertainty set, which takes into account historical traffic data. The second (on-line) stage implements a very efficient linear decision rule whose performance is guaranteed by the off-line computation. We test the proposed signal control procedure in a simulation environment that is informed by actual traffic data obtained in Glasgow, and demonstrate its full potential in on-line operation and deployability on realistic networks, as well as its effectiveness in improving traffic

Factors influencing success of Malaysian e-government implementation focused on websites assessment, infrastructure readiness and change management

The thesis explores the three main activities of websites assessment, infrastructure readiness test and change management as factors influencing the implementation of Electronic Government (EG) or Digital Government in Malaysia. The objectives of this research include understanding key factors that influence EG success in Malaysia, investigating the use of indicators within the factors that could facilitate measuring or monitoring the delivery and usage of online services, and developing a model based on factors and indicators that would help government agencies to deliver online services in EG. It is to facilitate delivery of online services and drive usage for the convenience of users or public at large. Mixed methods through triangulation evaluation approach where real life activities were emphasised with theoretical definitions for the case studies were used in seeking success of EG implementation. The first factor is the case study of the annual nationwide assessment and ranking of government agencies’ websites or portals. Websites were assessed based on a set of criteria and scored accordingly. These scores were then ranked according to 1-5 star ranking. The second factor is the case study with the activity of infrastructure readiness test for the bandwidth required for EG transactions over the internet. This was a crucial exercise since internet infrastructure is the basic requirement for EG implementation. The third factor is the case study of change management activity where awareness programmes and motivation practices towards respective agencies can be translated into usage increase of online services for the users. These case studies were conducted independently. Amongst the factors, the websites assessment was the key case study as it involved all government agencies nationwide and data of online services yearly could be gathered. The other two factors were to promote EG usage and have indirect contribution towards the delivery of online services. Selected agencies were used to correlate with the number of online services offered for a period of time against star ranking. A positive correlation between online services and the websites assessment rankings attained by the respective agencies was found. This is in line with the findings that using these factors increased number of online services and usage. A directional model illustrates the use of factors and indicators for future activities in implementing EG successfully

Evaluating the reliability of automatically generated pedestrian and bicycle crash surrogates

Vulnerable road users (VRUs), such as pedestrians and bicyclists, are at a higher risk of being involved in crashes with motor vehicles, and crashes involving VRUs also are more likely to result in severe injuries or fatalities. Signalized intersections are a major safety concern for VRUs due to their complex and dynamic nature, highlighting the need to understand how these road users interact with motor vehicles and deploy evidence-based countermeasures to improve safety performance. Crashes involving VRUs are relatively infrequent, making it difficult to understand the underlying contributing factors. An alternative is to identify and use conflicts between VRUs and motorized vehicles as a surrogate for safety performance. Automatically detecting these conflicts using a video-based systems is a crucial step in developing smart infrastructure to enhance VRU safety. The Pennsylvania Department of Transportation conducted a study using video-based event monitoring system to assess VRU and motor vehicle interactions at fifteen signalized intersections across Pennsylvania to improve VRU safety performance. This research builds on that study to assess the reliability of automatically generated surrogates in predicting confirmed conflicts using advanced data-driven models. The surrogate data used for analysis include automatically collectable variables such as vehicular and VRU speeds, movements, post-encroachment time, in addition to manually collected variables like signal states, lighting, and weather conditions. The findings highlight the varying importance of specific surrogates in predicting true conflicts, some being more informative than others. The findings can assist transportation agencies to collect the right types of data to help prioritize infrastructure investments, such as bike lanes and crosswalks, and evaluate their effectiveness

PERANAN STRATEGI ANTI FRAUD DAN AUDIT KEPATUHAN DALAM MENCEGAH FRAUD PADA INSPEKTORAT KABUPATEN CIAMIS

Penelitian ini difokuskan pada Strategi Anti Fraud dan Audit Kepatuhan dalam Mencegah Fraud (studi pada Inspektorat Kabupaten Ciamis). Adapun tujuan penelitian ini adalah 1]. Untuk mengetahui dan menganalisis strategi anti fraud dalam pencegahan fraud pada Inspektorat Kabupaten Ciamis. 2]. Untuk mengetahui dan menganalisis audit kepatuhan dalam pencegahan fraud pada Inspektorat Kabupaten Ciamis. 3]. Untuk mengetahui dan menganalisis hambatan penerapan strategi anti fraud dan audit kepatuhan dalam mencegah fraud pada inspektorat kabupaten ciamis. Metode yang digunakan dalam penelitian ini adalah metode deskriptif dengan teknik wawancara. Sedangkan untuk menganalisis data yang diperoleh digunakan analisis deskriptif. Hasil dari penelitian dan pengolahan data menunjukan bahwa strategi anti fraud yang dilakukan oleh Inspektorat Kabupaten Ciamis telah sesuai dengan SOP pemeriksaan namun masih pada tahap meminimalkan fraud tidak sampai menghilangkan fraud di Kabupaten Ciamis. Audit kepatuhan yang dilakukan oleh Inspektorat Kabupaten ciamis dalam pemeriksaan kepatuhan sudah dilakukan dengan SPIP yang ditetapkan berdasarkan keputusan Bupati Ciamis nomor 700/Kpts.385-Huk/2010. Kelemahan SDM, dan independensi yang menimbulkan conflict of interest dapat diminimalisir sehingga tindakan penyimpangan tersebut tidak akan mengganggu atau menggagagalkan instansi pemerintah mencapai tujuan dan sasarannya. Inspektorat Kabupaten Ciamis dalam kebijakan pengawasan APIP seharusnya menghilangkan konflik of interest. Dan terhadap pendekatan kepatuhan yang digunakan dalam pengawasan harus dipertahankan dan Kebijakan yang sedang berjalan direviu terus menerus untuk memastikan keefektifannya