The `Connected Car' concept has gained considerable interest in recent years from the car manufacturers. The `Connected Car' is envisioned to be connected to the Internet over wireless data networks enabling services like remote vehicle monitoring and diagnostics, real-time navigational assistance, sensor data collection and access to Internet media on the go. Although cellular data connectivity is largely ubiquitous in the developed world, it provides limited bandwidth and is quite expensive. Also, licensed spectrum is a scarce resource and cellular network providers are constantly faced with the challenge of providing good performance to an ever increasing user base. We posit that road-side WiFi networks have the potential to provide high bandwidth and inexpensive wireless connectivity to moving vehicles and can be beneficially used for offloading data away from the cellular data networks. In this dissertation we perform measurement analysis to show feasibility of such offloading. We also develop techniques to show how such offloading could be performed. We follow up with further techniques to improve vehicular WiFi access. First, we investigate a hybrid wireless access network design that integrates 3G and WiFi networks under vehicular mobility. The goal is to shift the load from the expensive 3G networks to the less expensive WiFi network without hurting the user experience. Instead of simply striping data over two network connections, we develop a utility and cost-based formulation that decides the right amount of load that can be put on the 3G network to maximize users benefit. We develop and experiment with a scheduler to do this. We show via extensive measurements on a metro-scale WiFi network and a nationwide 3G network that the hybrid design is able to deliver much superior mobile video streaming experience for the user while reducing the load on the 3G network by three-fourth. Then, we focus our attention to improving vehicular WiFi performance. We argue that mobility and connectivity information along drives can be predicted with good accuracy using historical information such as GPS tracks and RF fingerprints. We exploit such information to develop new handoff and data transfer strategies to reduce connection establishment latency and to improve download performance. Next, we develop Brave - an SNR-based rate adaptation algorithm for vehicular WiFi access environments. Because of the highly dynamic nature of the outdoor vehicular WiFi link, BRAVE only considers short history to make rate selection decisions and out-performs other well-known frame-based and SNR-based algorithms. Finally, we show how a multi-radio multi-vehicle system can improve the perceived coverage and throughput performance of vehicular WiFi clients. Using a metro-scale WiFi deployment we experimentally demonstrate that with intelligent access point filtering, a single multi-radio vehicular client can effectively mask connection establishment latencies completely and using another such vehicle as a relay can mask coverage holes to a large extent.