# Research on the Impact of Flexible Working Hours on Reducing Traffic Delays in the City
## 1 Introduction  
With the rapid progress in industrialization and urbanization, it is expected that there will be an increasing number of vehicles in the transportation system, potentially leading to social problems such as traffic congestion, accidents, and environmental pollution [1; 2]. In addition, exhaust gases from traffic contribute to the greenhouse effect [3; 4; 5]. The current primary challenge for urban systems is addressing the negative impacts of road transport. It is known that approximately 50% of pollution comes from road traffic, mainly from combustion engines [1]. In recent decades, various measures have been adopted to reduce pollution, including vehicle technologies, alternative fuels, and sophisticated demand management policies [6; 7]. Among the most demanding traffic flow management strategies is addressing traffic congestion at the network level. At intersections, unnecessary slowing down or stopping of vehicles results in wasted fuel and time, as well as decreased driver and passenger comfort [8]. More than 40% of traffic accidents, including 20% of fatalities, occur at intersections. Most of them are caused by human factors [9]. Urban traffic congestion affects daily life, and the imbalance between supply and demand in transportation causes these problems [10; 11; 12]. Currently, traffic congestion in large cities is one of the most critical issues affecting daily life. The imbalance between supply and demand in transportation leads to traffic jams [13; 14]. One way to achieve balance is to increase the transportation supply, for example, by constructing roads and intersections. However, implementing such projects is costly and sometimes impossible due to space constraints. An alternative solution is demand management, which is less expensive and involves reducing demand through traffic management plans [15; 16]. Online shopping has proven to be an effective way to reduce urban traffic. Traffic volume forecasts are made to balance supply and demand [17; 18; 19]. The choice of mode of transport and travel time influences the demand for urban transportation [20]. One approach to demand management is the proper allocation of travel time. Flexible working hours (WHs) can reduce traffic congestion. Employees can choose their start time within a predetermined period, selecting a less congested time for travel or a shared vehicle with family members. Remote work and flexible WHs have increased [21; 22]. Consequently, it is expected that these individuals will choose their commuting time for two reasons to help manage travel demand. Firstly, an individual may choose to travel when traffic load is lighter. Secondly, an individual may choose to travel when a shared vehicle can be used with other family members [23]. Remote work and flexible WHs have been increasing in recent years [24]. This paper aims to model this issue and implement it in the city of TopoIcany, to optimize company start times with minimal deviations from original schedules. Research by the authors on this topic indicates that the implementation of travel time selection policies can significantly reduce traffic density in urban networks. Studies show that flexible WHs decrease car usage and traffic congestion, especially during peak hours. In the Netherlands, adjusting WHs and working from home reduced travel delays. Similar results are presented for public transport. In another study by [PERSON] et al., research shows that in the Netherlands, working from home and adjusting WHs to avoid peak times are the most significant forms of flexible work [25; 26]. [PERSON] et al. [27] proposed a mathematical model to reduce traffic congestion after the implementation of flexible WH policies. This model aims to find the WHs of each target node considering travel demand between origin and target nodes, routes, and road capacities. Finally, using the proposed program, traffic density during peak hours was significantly reduced. In Beijing, flexible WHs reduced traffic volume by 15.24% and average travel time by 21.73%.