Traffic App Research Articles

Mobile applications for road traffic health and safety in the mirror of the Haddon\u2019s matrix

BackgroundRoad traffic accidents have been one of the leading causes of death. Despite the increasing trend of road traffic apps, there is no comprehensive analysis of their features and no taxonomy for the apps based on traffic safety theories. This study aimed to explore the characteristics of available mobile apps on road traffic health/safety and classify them with emphasis on Haddon’s matrix.MethodsThe researchers examined the mobile applications related to road traffic health/safety using qualitative content analysis. Google Play was searched using a combination of the keywords. Haddon’s matrix was applied to analyze and classify those mobile apps residing in the categories of Road Traffic health & Safety, and Road Traffic Training.ResultsOverall, 913 mobile apps met the inclusion criteria and were included in the final analysis. Classification of the apps based on their features resulted in 4 categories and 21 subcategories. A total number of 657 mobile apps were classified based on Haddon’s matrix. About 45.67% of these apps were categorized as the road traffic health & safety group.ConclusionsHaddon’s matrix appears to have the potential to reveal the strengths and weaknesses of existing mobile apps in the road traffic accident domain. Future development of mobile apps in this domain should take into account the existing gap.

How Citizen Demand Affects the Process of M-Government Adoption: An Empirical Study in China

Mobile government applications have been increasingly adopted by governments around the world, but their use by targeted clients is lower than expected and under-examined in the literature. We conceptualize citizen adoption of m-government as a dynamic process composed of three interrelated stages—awareness, current use, and future use. However, we suggest that citizen demand will affect them differently. To test this, we used recent survey data from a Chinese city to examine the antecedents of citizen adoption of an m-government application, a traffic APP. The results reveal that citizens’ commuting demands significantly affected their awareness of the APP, but did not impact their current and planned future uses, which were primarily affected by citizens’ perceptions and habits. These findings help open up the “black box” of citizen adoption, and generate policy suggestions for boosting citizen use, of m-government.

Continuance Intention in Traffic-Related Social Media: A Privacy Calculus Perspective

Thanks to technological breakthroughs, drivers are enjoying traffic-associated social media to share and update information in place of the traditional ways using TV or radio. In this research, we integrate the IS success model and the privacy calculus model to elaborate on the factors of sustaining user’s continuance intention in information-sharing platforms. We collected data via online surveys from a community-based application that supports real-time traffic information sharing. The findings shed light on how perceived value and privacy concerns impact users’ continuance intention. While a higher level of perceived value enhances users’ continuance intention, privacy concerns do not influence users’ decision in continuing using the traffic app. This research provides initial efforts to investigate the effect of privacy paradox on users' continuance intention in the new context of a specific traffic-related social media platform.

Monitoring vessel traffic in Rio de Janeiro port area: Control of marine antifouling regulations

Nowadays, we live in a global interdependence time, without precedents, in which the national boundaries do not represent significant barriers to the trade between nations anymore. According to the International Marine Organization (IMO), more than 90% of all the global commercial transport flows through sea. This heavy vessel traffic produces a lot of impact and changes in the marine environment. Each vessel of tonnage higher than 100t has a registration number on IMO that guarantees that the vessel is by the security, health and environment conventions of the Organization. The present study has a goal of developing a methodology to monitor the vessel movement in Rio de Janeiro port area, for verifying the presence of vessels non-signatories of the A928 convention (The International Convention on the Control of Harmful Antifouling Systems on Ships). The data were obtained from the Marine Traffic app, in the period between December of 2015 and October of 2017. Data such as registration number on IMO, homeport, dates, and registration flag was compiled for 3015 vessels. Those that were derived from non-signatories countries to the AFs Convention represented 3.78% of the total of monitored vessels. This percentage is lower than the global tonnage of the non-signatories countries, which is 6.45%. The proposed method is laborious but proved to be efficient to indicate the possibility of environmental issues related to marine traffic, even before a ship's arrival in the destination port.

Analysis and classification of the road traffic health and safety mobile apps based on the Haddon’s matrix

Background: Road traffic accidents have been one of the main causes of death worldwide. However, most of the crashes are both predictable and preventable. With the widespread use, mobile phone is considered as a major road safety risk. However, varieties of apps have recently been developed for improving the road traffic health and safety. Despite an increasing trend of these apps, there is no comprehensive analysis of their features and no taxonomy or classification of them based on the traffic safety theories or frameworks. The aim of this study was to explore characteristics of available mobile apps on the road traffic health and safety and classify them based on the Haddon’s Matrix. Methods: To conduct this research, a comprehensive and systematic review of the mobile applications developed for the road traffic health and safety was carried out through using the qualitative content analysis. Google Play was searched through using a combination of the keywords to retrieve the road traffic apps. In order to extract the app features, their description was examined, and their content was analyzed. Then they were classified in four main categories including Road Traffic Health & safety, Road Traffic Training, Road traffic Navigation, and Other Road Traffic apps. Finally, the Haddon’s matrix was applied to analyze and classify those mobile apps residing in two main categories of the traffic health & safety, and the traffic training. Haddon’s matrix is a relevant framework for structured analyses of traffic injury events. In the Haddon’s matrix, the contributions of human, vehicle/equipment and environmental factors to the injuries in the three phases pre-crash, crash and post-crash has been presented. Results: In this study, 916 mobile apps met the inclusion criteria and were included in the final analysis.19 subcategories were identified for classifying the included apps based on their features and functionalities. In total, 620 of the mobile apps were grouped on the basis of Haddon’s matrix. About 61.76% of these apps were categorized as the traffic health & safety group. Among the subcategories of the traffic health & safety group, the highest number of apps (194) was the real-time traffic alerting apps. Behavior based feed-backing subcategory was ranked the second among the traffic health and safety apps with 75 apps. When the researchers applied factors and phases of Haddon’s framework for analyzing the apps, it was found that the Haddon’s factors have been taken into account in the apps grouped into two main categories of the apps including traffic training and traffic health & safety applications. The highest percentage of Haddon’s matrix factor considered for the intervention through the apps was related to the physical & social environment (23.69%) in the traffic training category. The features of 259 apps were classified in the Event/ Driving phase of Haddon’s matrix. Most of the apps in this phase was related to the real-time traffic alerting subgroup. These apps performed in real time while driving. The lowest number of the apps in driving phase was observed in the behavior based feed backing subcategory, which inform drivers about their driving behavior in real-time. Sixteen apps had features that categorized them in both Pre-event / Pre-driving and Event / Driving phases. In addition, 20 apps were found to have feature for intervening in both Event / Driving and Post-event / Post –driving phases. All 235 apps in the traffic training category were in pre-event/ pre-driving phase, which requires users to use these apps before driving. Conclusion: Applying Haddon’s matrix on analyzing and classifying the traffic health and safety apps revealed strengths and weaknesses of the existing related mobile apps in terms of the factors that must be considered for intervention toward prevention and reduction of road traffic accidents as the main public health issue.

The use of context-sensitive insurance telematics data in auto insurance rate making

Historically, auto insurers use various socio-demographic underwriting factors to differentiate driver risks. With the invention of GPS devices, information such as mileage, traffic condition and driving habits can be incorporated into auto insurance premium calculation. Several major auto insurance companies have offered usage based insurance (UBI) programs where auto insurance premiums are sensitive to actual GPS readings combined with driver’s driving behavior. However, given that telematics data are proprietary to the insurance companies that collect such data, the accessibility of UBI is extremely limited.In this research we showcase how second-by-second GPS data can be integrated into existing or new auto insurance pricing structures. We use two types of data: real-time GPS trajectory data collected using a traffic app, as well as survey data. We incorporate vehicle trajectories and accident data to quantify the relationship between driving hazard and accidents with the goal of establishing the linkage between driving risks and accident costs. GPS data considered in this study include not only tradition UBI factors, but also the unique contextual-based risk measurements that compares the driving speed of the vehicle with other drivers on the same road segment. Although smartphone data is used in this study, the methodology developed can be applied to GPS trajectory data collected from other devices such as on-board diagnostics (OBD) or black box solutions. We find that hard brakes, hard starts, peak time travel, speeding as well as driving at a speed significantly different from traffic flow are highly correlated with accident rate. We illustrate the potential underwriting loss an insurer may incur resulting from adverse selection if omitting pertinent risk factors. The results of our study can help insurance companies that are interested in getting into the UBI area set up their auto insurance premium rates.

The Car as an Ambient Sensing Platform [Point of View

In recent years, cars have evolved from purely mechanical to veritable cyberphysical systems that generate large amounts of real-time data. These data are instrumental to the proper working of the vehicle itself, but make them amenable to a multitude of other uses. For instance, GPS information has recently been used for a large number of mobility studies in the academic community [1] , [5] , as well as to feed traffic apps such as Google Traffic and Waze. This use of vehicle data is already having a profound impact in science, industry, economy, and society at large. Now, imagine that instead of accessing one single source of vehicle-generated data (GPS), one can access the entire wealth of data exchanged on the controller area network (CAN) bus in near real timeamounting to over 4000 signals sampled at high frequency, corresponding to a few gigabytes of data per hour. What would be the implications, opportunities, and challenges sparked by this transition?

User requirements and route choice response to smart phone traffic applications (apps)

Alongside the advent of smart phones, the development of traffic information applications (apps) has revolutionized the way drivers obtain the latest traffic information and guidance. Traffic information dissemination via apps show added advantage and is believed to have positive effects on changing travel behavior. This study aims to investigate user requirements on apps feature that are likely to contribute to a higher market penetration rate. Besides, it aims to evaluate the impact of traffic apps on drivers’ route choice behavior. Two separate stated preference questionnaire studies were conducted at major spots in the Klang Valley region of Malaysia. Drivers are shown with various scenarios that consist of different characteristics of apps and routes. Bivariate probit models were developed to assess the drivers’ choices. It is anticipated that the findings from the study would be useful to the authorities when formulating traffic information dissemination policies.

Considerations in the diffusion of a public traffic app for Metro Manila

The proliferation of apps enabling motorists to access traffic information on the move introduces several intriguing possibilities for transportation authorities. In the expectation that public demand for these apps will increase as both road traffic and smartphone ownership rise, these tools invite consideration alongside other traffic management tools. Traffic app-specific considerations include increased motorist interactivity, consumer technology diffusion, and institutional factors affecting the rollout of e-government initiatives. This article uses the introduction of the MMDA Traffic Navigator—a Manila-based, publicly developed traffic app—to illustrate these considerations. Additionally, app usage among residents of nearby cities constituting Manila’s extended metropolitan region is considered. These considerations are subsequently classified using a 4E framework under economy, effectiveness, efficiency, and equity to inform other authorities’ efforts to either incorporate traffic apps into overall transport planning or even take the initiative in developing them.

Design of Driving Behavior Pattern Measurements Using Smartphone Global Positioning System Data

The emergence of new technologies such as GPS, cellphone, Bluetooth device, etc. offers opportunities for collecting high-fidelity temporal-spatial travel data in a cost-effective manner. With the vehicle trajectory data achieved from a smartphone app Metropia, this study targets on exploring the trajectory data and designing the measurements of the driving pattern. Metropia is a recently available mobile traffic app that uses prediction and coordinating technology combined with user rewards to incentivize drivers to cooperate, balance traffic load on the network, and reduce traffic congestion. Speed and celeration (acceleration and deceleration) are obtained from the Metropia platform directly and parameterized as individual and system measurements related to traffic, spatial and temporal conditions. A case study is provided in this paper to demonstrate the feasibility of this approach utilizing the trajectory data from the actual app usage. The driving behaviors at both individual and system levels are quantified from the microscopic speed and celeration records. The results from this study reveal distinct driving behavior pattern and shed lights for further opportunities to identify behavior characteristics beyond safety and environmental considerations.